Best course on data science in Hyderabad that makes you learn hands-on

Do you want to know the details of a course that makes you learn hands on? Then read on.Consider learning Data Science as it helps you to stay relevant as a IT professional. Upgrade yourself by attending this data science course in Hyderabad . It is taught by an expert instructor who is also a real time data scientist.Anyone can join this training , irrespective of your background. There are multiple institutes which teach just theoretical concepts. But that is not enough to truly master this exciting field.

Why should you join a data science course?

The reason is that we are living in a digital world where everything is driven by data.In order to take advantage of the data in their decision making process,every organization wants to find ways to use Data Science to help them grow their business.Hence Data Science has become one of the hottest sectors right now and can continue to grow for the next 10-15 years.Joining a data science course is the right path to get into this sector.

Who should study a data science course?

Following are the people who should consider doing a data science course to get to the next step in their career.

  • Professionals who already have some experience using other programming languages and are looking for a career shift.
  • Python or R developers.
  • Big data engineers.
  • Data analysts.
  • Those who use SQL , BI and Datawarehousing tools and love working with data in depth.
  • Software engineers who have a mathematical or statistical background.
  • Data visualization specialists.
  • Those who have indepth knowledge in a functional domain.

Master Real World Data Science Skills By Learning From An Expert

Level up by mastering all the topics necessary for you to become a sought after data scientist!

Why You Should Choose us For Data Science Training In Hyderabad ?

You should choose us because we offer the best in class training in Hyderabad. When you are considering to join any training course like this, think about the impact that it will have on your career. You should be able to crack the data scientist interviews, which is only possible when every concept is clearly understood.Often students attend MOOCs(Massive Open Online Courses) such as Udemy, which are a good deal when it comes to price, but not from the value perspective. Their doubts are not clarified due to lack of access to the faculty.You should not learn just theory and get trained from short videos, as you cannot clarify your doubts.We dont want you to study from incomplete blog posts which give partial knowledge.

This is where we are different. Our course is taught by a professional data scientist and you will have access to him to clarify your doubts any time. We make sure that all the concepts are clearly understood by our students. Time is also an important factor for choosing any software course.Some of you might be working professionals or students who may not have much time to learn the new technologies.If you start reading textbooks on data science, it will take a long time.We know that you want to hit your target of becoming a data scientist quickly.At the same time, the market wants people that can deliver results.So we offer targeted training that can deliver results for you. This course will be your shortest path to becoming a data scientist.

What are the job opportunities available for Data Science professionals?

There are myriad of Job opportunities available for Data Science practitioners.

  • Analytics Manager
  • Business Analyst
  • Business Intelligence(BI) Analyst
  • Data Analyst
  • Data Scientist
  • Research Analyst
  • Research Scientist
  • Senior Data Analyst
  • Statistician
  • Data Architect

What are the salaries for Data Scientists?

The average base pay for a Data Scientist in India, as per recent Glassdoor report is Rs.1,033K/yr. This is quite high considering that this is the average across all the experience levels, across different locations. In the US, data scientists can expect to make $120,495/yr on average.The better your hands-on expertise is, the better you will be valued in the job market.

How is your online training different from classroom sessions?

In online training,you can attend the class from your current location, thus avoiding the hassle of travel. You can even record the live online session and use it for revision later. In a classroom session,you will need to physically travel to the place where the trainer is conducting the session. Hence online training is more flexible for a student to attend classes from any part of the world.

Can I attend other batches, if I already enrolled for course?

Yes, you can attend other batches as well. Send in your interest personally to me post attending your first batch. I will send the live login link to you.

Is this live training or a pre-recorded session?

This is a live training.You can clarify all your doubts on a topic during the session itself. Unlike pre-recorded classes, you can interact with me during the class , and even post completing the batch.

What Is Data Science? Who Is Data Scientist?

Data Science is the study of data using scientific approaches in order to extract useful insights.Often, companies have troves of information from which they can make strategic decisions or predict performance of the business in future.The very exercise of analyzing data, manipulating it, and trying to get some answers to the questions constitutes Data Science. A Data Scientist is someone who extracts the data , cleans it, manipulates it, visualizes it , and explains it to a non technical audience, such  that actionable information can be used by the business.

What is machine learning?

In machine learning, an algorithmic model will be built based on the inferences from existing data. This model will be used to predict the outcome when new data is provided. So here you are teaching a machine to take decisions without explicitly programming. On the other hand, in Data Science we use techniques from mathematics, statistics, and computer science to derive insights from existing data.

Many courses in Hyderabad do not teach you the actual scenarios needed to work in either of these fields. They cover the basics and leave it to the student to work on the projects themselves.The problem with that approach is that you will learn the concepts theoretically and not with case studies. The actual challenge will be when you are asked to work on real time data. You will neither be able to clean it nor visualize it. Attending such courses will not be useful to you. You will be lagging behind your competitors, as they will have the advantage of getting practical exposure.

Why we are The Best Data Science Training Institute In Hyderabad ?

We are the best Data Science certification online training in Hyderabad due to the freshness of our course content.Technologies in the data science field keep changing at a rapid pace.We've shaped our content to fit your needs to ensure we can deliver the most up to date concepts. Our training program also inculcates you how to consistently keep yourself updated, which is missing in most of the trainings available in the market.Students who are on top of changing industry standards have a unique advantage in the market, and they are always ahead of their competitors.

Hyderabad: The unofficial IT training hub of India

Students from across India come to Hyderabad to learn the latest software technologies. The reason is that the fees is very less here, compared to other Indian cities. To cater to the demand, a number of institutes have started in Ameerpet, and are now spreading across different areas like Madhapur, KPHB, etc.Before enrolling in any data science training, think whether you are really passionate and can dedicate time to understand it. Unlike other fields of computer science, it requires a lot of consistent practice to understand the nuances of different concepts in this industry.

Programming languages taught in these classes on data science

PythonRSQL
1.Easiest to learn general purpose programming language.1.Open source language for statistical computing1.Non-procedural language used for querying data stored in RDBMS.
2.Maintained by the Python Software Foundation2.Maintained by the R Foundation for Statistical Computing2.Maintained by the American National Standards Institute(ANSI)
3.Used in web development and scientific computing3.Used in statistical model development3.Used in OLTP application development.

It is suggested that you study to become an expert in one of these languages, say Python, and be aware of others. This is because Python is all set to overtake R as the language of choice for data scientists. So ample opportunities will be on your way if you can master Python.

The learning outcomes of this data science training

While the big data analytics field is constantly evolving, you need to have skills across multiple disciplines to be employable. This data science course aims to build your competencies across these data analysis competencies - Basic Math, Statistics, Calculus , Multi-variate , Algebra, Linear and Quadratic models , Integer Programming , PearsonR, MLlib, Lambda Functions or Chi-Square , Tests for Significance and Standard Deviation.

Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician

A major portion of our curriculum will be on these programming languages and tools - R, Python , Excel , VBA , HTML , SQL, Spark , TensorFlow ,Radiant, Tableau / QlikView , SAS , Data Wrangling , Hadoop. Before joining data science courses, check whether you will be taught these subjects. These tools are needed to ensure a scientific approach to data collection and analysis, while building these critical abilities.

  • Data Management / Governance
  • Strategic Thinking to solve a problem using data science
  • Hacking skills.
  • Organization (of data, of concepts, of priorities)
  • Data Visualization in Tableau / QlikView
  • Communication(Written & Verbal)

Another learning objective of this syllabus is to teach you machine learning concepts like Decision Trees, Ordinary, Neural, Vectors, Clustering, Independent Component Analysis. The course also focuses on Natural Language Processing, Amazon Machine Learning, Azure ML, Caffe, H2O, Massive, Spark MLlib, mlPack, Patterns, Shogun, Torch and Tensorflow. Does any Ameerpet institute have such content? By now, I think you would have got an idea about this educational program – it is more valuable than other ones available in the market.

Data Science Course Topic Outlines

  • Programming
    • Introduction to R - Vectors, Matrices, Factors, Data frames,Lists
    • Intermediate R - Conditionals and Control Flow,Loops, Functions, The apply family, Utilities
    • Introduction To Python - Python Basics, Lists, Functions and Packages, Numpy
    • Intermediate Python for Data Science - Matplotlib,Dictionaries and Pandas,Logic, Control Flow and Filtering,Loops,Case Study: Hacker Statistics
    • Python Data Science Toolbox - Using iterators in Python,List comprehensions and generators,Writing your own functions,Default arguments, variable-length arguments and scope,Lambda functions and error-handling
    • Object-Oriented Programming in R:S3 and R6 - Introduction to Object-Oriented Programming, Using S3, Using R6, R6 Inheritance, Advanced R6 Usage
    • Scalable Data Processing in R - Working with increasingly large data sets, Processing and Analyzing Data with bigmemory, Working with iotools, Case Study: A Preliminary Analysis of the Housing Data
    • String Manipulation in R with stringr - String basics,Introduction to stringr,Pattern matching with regular expressions, More advanced matching and manipulation, Case studies
    • Writing efficient R code - The Art of Benchmarking, Fine Tuning: Efficient Base R, Diagnosing Problems: Code Profiling, Turbo Charged Code: Parallel Programming
    • Data Types For Data Science - Fundamental data types, Dictionaries - the root of Python, Meet the collections module, Handling Dates and Times,Answering Data Science Questions
    • Optimizing R Code with Rcpp - Functions and Control Flow,Vector classes,Case Studies
    • Introduction to the Tidyverse - Data wrangling,Data visualization,Grouping and summarizing,Types of visualizations
    • Introduction to Shell For Data Science - Manipulating files and directories, Manipulating data, Combining tools, Batch processing, Creating new tools
    • Developing R Packages -The R Package Structure, Documenting Packages, Checking and Building R Packages, Adding Unit Tests to R Packages.
    • Data Manipulation in R With data.table - Introduction to data.table, Selecting and Computing on Columns, Groupwise Operations, Reference Semantics, Importing and Exporting Data
    • Working with Dates and Times in R -Dates and Times in R, Parsing and Manipulating Dates and Times with lubridate, Arithmetic with Dates and Times, Problems in practice.
    • Introduction to Git for Data Science - Basic workflow, Repositories, Undo, Working with branches, Collaborating
    • Python for R Users - The Basics, Control flow, Loops, and Functions , Pandas, Plotting, Capstone.
    • Conda Essentials - Installing Packages, Utilizing Channels, Working with Environments, Case Study on Using Environments
    • Foundations of Functional Programming with purrr - Simplifying Iteration and Lists With purrr, More complex iterations, Troubleshooting lists with purrr, Problem solving with purrr
    • Parallel Programming in R - Can I Run My Application in Parallel?, The parallel Package, foreach, future.apply and Load Balancing, Random Numbers and Reproducibility
    • Foundations of Functional Programming with purrr - Simplifying Iteration and Lists With purrr, More complex iterations, Troubleshooting lists with purrr, Problem solving with purrr
    • Conda for Building & Distributing Packages - Anaconda Project,Python Packages,Conda Packages
    • Python for MATLAB Users - From MATLAB to Python,NumPy and Matplotlib,Dictionaries and pandas,Control Flow and Loops
    • Intermediate SQL Server - Summarizing Data,Math Functions,Processing Data in SQL Server,Window Functions
    • Intermediate Spreadsheets for Data Science - What's in a cell,Working with numbers,Logic and Errors,Positional Matching
    • Writing Functions and Stored Procedures in SQL Server - Temporal EDA, Variables & Date Manipulation, User Defined Functions,Stored Procedures, NYC Taxi Ride Case Study
    • Spreadsheet Basics - formulae, referencing, absolute references, autofilling, and reactivity
    • Data Analysis with Spreadsheets - Predefined functions, Conditional functions and lookups
    • Supply Chain Analytics in Python - Basics of Supply Chain Optimization and PuLP, Modeling in PuLP, Solve and Evaluate Model, Sensitivity and Simulation Testing of Model
    • Fraud Detection in R - Social network analytics, Imbalanced class distributions , Digit analysis and robust statistics
    • Analyzing Social Media Data in Python - Basics of Analyzing Twitter Data, Processing Twitter text,Twitter Networks, Putting Twitter data on the map
    • GARCH Models in R - The standard GARCH model as the workhorse model,Improvements of the normal GARCH model,Performance evaluation,Applications
    • Longitudinal Analysis in R - Introduction to Longitudinal Data, Modeling Continuous Longitudinal Outcomes, Add fixed predictor variables, Modeling Longitudinal Dichotomous Outcomes
    • Object-Oriented Programming in Python - Getting ready for object-oriented programming, Deep dive into classes and objects, Fancy classes, fancy objects Inheritance, polymorphism and composition
    • Fraud Detection in Python - Introduction and preparing your data, Fraud detection using labelled data, Fraud detection using unlabelled data, Fraud detection using text
    • Advanced Dimensionality Reduction in R - Introduction to Advanced Dimensionality Reduction, Introduction to t-SNE, Using t-SNE with Predictive Models, Generalized Low Rank Models (GLRM)
    • Customer Segmentation in Python - Cohort Analysis, Recency, Frequency, Monetary Value analysis, Data pre-processing for clustering, Customer Segmentation with K-means
    • Topic Modeling in R - Quick introduction to the workflow, Wordclouds, stopwords, and control arguments, Named entity recognition as unsupervised classification, How many topics is enough?
    • Feature Engineering in R - Creating Features from Categorical Data, Creating Features from Numeric Data, Transforming Numerical Features, Advanced Methods
    • Defensive R Programming - Avoiding Conflict, Early warning systems, Preparing your defenses, Creating a Battle Plan Foundations of Predictive Analytics in Python (Part 2) - Crucial base table concepts, Creating variables, Data preparation, Advanced base table concepts
    • Analyzing US Census Data in Python - Decennial Census of Population and Housing, American Community Survey, Measuring Segregation, Exploring Census Topics
    • Big Data Fundamentals via PySpark - Introduction to Big Data analysis with Spark,Programming in PySpark RDD’s,PySpark SQL & DataFrames,Machine Learning with PySpark MLlib
    • Dimensionality Reduction in Python - Exploring high dimensional data, Feature selection I, selecting for feature information,Feature selection II, selecting for model accuracy,Feature extraction
    • Working with Dates and Times in Python - Dates and Calendars,Combining Dates and Times, Time Zones and Daylight Saving,Easy and Powerful: Dates and Times in Pandas
    • Designing Machine Learning Workflows in Python - The Standard Workflow,The Human in the Loop,Model Lifecycle Management,Unsupervised Workflows
    • Software Engineering for Data Scientists in Python - Software Engineering & Data Science, Writing a Python Module, Utilizing Classes, Maintainability
    • SQL for Exploratory Data Analysis - What's in the database?, Summarizing and aggregating numeric data, Exploring categorical data and unstructured text,Working with dates and timestamps
    • Intermediate SQL - We'll take the CASE, Short and Simple Subqueries, Correlated Queries, Nested Queries, and Common Table Expressions, Window Functions
    • Writing Efficient Python Code - Foundations for efficiencies, Timing and profiling code, Gaining efficiencies, Basic pandas optimizations
    • Introduction to Data Science in Python - Getting Started in Python, Loading Data in pandas, Plotting Data with matplotlib, Different Types of Plots
    • Introduction to Relational Databases in SQL - Your first database, Enforce data consistency with attribute constraints, Uniquely identify records with key constraints, Glue together tables with foreign keys
    • Introduction to SQL Server - SELECTion Box, Groups, strings, and counting things, Joining tables, CREATE , INSERT ,UPDATE, DELETE
    • Introduction to Matplotlib - Plotting time-series,Quantitative comparisons and statistical visualizations, Sharing visualizations with others
    • Introduction to Text Analysis in R - Wrangling Text, Visualizing Text, Sentiment Analysis,Topic Modeling
    • Feature Engineering for Machine Learning in Python - Creating Features, Dealing with Messy Data, Conforming to Statistical Assumptions,Dealing with Text Data
    • Reporting in SQL - Exploring the Olympics Dataset, Creating Reports,Cleaning & Validation,Complex Calculations
    • Foundations of Probability in Python - Let's start flipping coins, Calculate some probabilities,Important probability distributions,Probability meets statistics
    • Optimizing Python Code with pandas - Select columns and rows efficiently,Replace values of a DataFrame using the .replace() function,Speed efficient methods for iterating through a DataFrame,Data manipulation for groups using the .groupby() function
    • Python for Spreadsheet Users - Diving In,Pivoting in Python,Working with Multiple Sheets,Plotting Data
    • Improving Your Data Visualizations in Python - Highlighting your data,Using color in your visualizations,Showing uncertainty,Visualization in the data science workflow
    • Clustering Methods with SciPy - Introduction to Clustering,Hierarchical Clustering,K-Means Clustering,Clustering in Real World
    • Analyzing Marketing Campaigns with pandas - Pandas,Exploratory Analysis & Summary Statistics,Conversion Attribution,Personalization A/B Test
    • Financial Analytics in Spreadsheets - Monitoring historical prices,Monitoring historical returns,Monitoring the distribution of returns,Benchmarking performance
    • Machine Learning with Apache Spark - Introduction,Classification,Regression,Ensembles & Pipelines
    • Introduction to TensorFlow in Python - Introduction to TensorFlow,Linear Regression in TensorFlow,Neural Networks in TensorFlow,High Level APIs in TensorFlow
    • Model Validation in Python - Basic Modeling in scikit-learn,Validation Basics,Cross Validation,Selecting the best model with Hyperparameter tuning.
    • Hyperparameter Tuning in Python - Hyperparameters and Parameters,Grid search,Random Search,Informed Search.
    • Introduction to Seaborn - Introduction to Seaborn,Visualizing Two Quantitative Variables,Visualizing a Categorical and a Quantitative Variable,Customizing Seaborn Plots
    • Improving Query Performance in SQL Server - Introduction, Review and The Order of Things, Filtering and Data Interrogation,Sub-queries and presence or absence,Query performance tuning
    • Analyzing Business Data in SQL - Revenue, cost, and profit, User-centric KPIs,ARPU, histograms, and percentiles,Generating an executive report
    • Ensemble Methods in Python - Combining Multiple Models, Bagging, Boosting, Stacking
    • Exploratory Data Analysis in Python - Read, clean, and validate, Distributions,Relationships,Multivariate Thinking
    • Generalized Linear Models in Python - Introduction to GLMs,Modeling Binary Data,Modeling Count Data,Multivariable Logistic Regression
    • Data-Driven Decision Making in SQL - Introduction to business intelligence for a online movie rental database,Decision Making with simple SQL queries,Data Driven Decision Making with advanced SQL queries,Data Driven Decision Making with OLAP SQL queries
    • Deep Learning with PyTorch - Introduction to PyTorch,Artificial Neural Networks,Convolutional Neural Networks (CNNs),Using Convolutional Neural Networks
    • Analyzing IoT Data in Python - Accessing IoT Data,Processing IoT data,Analyzing IoT data,Machine learning for IoT
    • Improving Query Performance in PostgreSQL - Bringing Together the Data, Minimizing Results and Decreasing the Load, Using Database Designed Properties,Assessing Query Performance
    • Preparing for Statistics Interview Questions in Python - Probability and Sampling Distributions,Exploratory Data Analysis,Statistical Experiments and Significance Testing,Regression and Classification
    • Cleaning Data with Apache Spark in Python - DataFrame details,Manipulating DataFrames in the real wold,Improving Performance,Complex processing and data pipelines
    • Feature Engineering for NLP in Python - Basic features and readability scores,Text preprocessing, POS tagging and NER,N-Gram models,TF-IDF and similarity scores
    • Streamlined Data Ingestion with pandas - Importing Data from Flat Files,Importing Data From Excel Files,Importing Data from Databases,Importing JSON Data and Working with APIs
    • Sentiment Analysis in Python - Sentiment Analysis Nuts and Bolts,Numeric Features from Reviews,More on Numeric Vectors: Transforming Tweets,Let's Predict the Sentiment
    • Survey and Measurement Development in R - Preparing to analyze survey data,Exploratory factor analysis & survey development,Confirmatory factor analysis & construct validation,Criterion validity & replication
    • Regular Expressions in Python - Basic Concepts of String Manipulation,Formatting Strings,Regular Expressions for Pattern Matching,Advanced Regular Expression Concepts
    • Forecasting Using ARIMA Models in Python - ARMA Models,Fitting the Future,The Best of the Best Models,Seasonal ARIMA Models
    • Data Science for Managers - Introduction to Data Science,Data Collection and Storage,Analysis and Visualization,Prediction
    • Dimensionality Reduction in R - Principal component analysis (PCA), Advanced PCA & Non-negative matrix factorization (NNMF),Exploratory factor analysis (EFA),Advanced EFA
    • Inference for Numerical Data - Bootstrapping for estimating a parameter,Introducing the t-distribution,Inference for difference in two parameters,Comparing many means
    • Inference for Categorical Data - Inference for a single parameter,Proportions: testing and power,Comparing many parameters: independence,Comparing many parameters: goodness of fit
    • Customer Analytics & A/B Testing in Python - Key Performance Indicators: Measuring Business Success,Exploring and Visualizing Customer Behavior,The Design and Application of A/B Testing,Analyzing A/B Testing Results
    • Parallel Programming in R - Can I Run My Application in Parallel?, The parallel Package, foreach, future.apply and Load Balancing, Random Numbers and Reproducibility
    • Time Series with data.table in R - Review of data.table,Getting time series data into data.table,Generating lags, differences, and windowed aggregations, Case study: financial data
    • Data Visualization in Spreadsheets - Business Intelligence and Using Dashboards,Efficient Column Charts,Dashboard Controls,Other Charts for Your Dashboard,Conditional Formatting
    • R For SAS Users - Getting Started with R,Data Wrangling, Data Exploration, Models and Presentation
    • Creating Robust Python Workflows - Python Programming Principles, Documentation and Tests, Shell superpowers,Projects, pipelines, and parallelism
    • Statistics in Spreadsheets - Getting To Know Your Data, Statistical Data Visualization, Statistical Hypothesis Testing,Case Study: Dating Profile Analysis
    • Introduction to Spark SQL with Python - Pyspark SQL,Using window function sql for natural language processing, Caching, Logging, and the Spark UI,Text classification
    • Conditional Formatting in Spreadsheets - A Primer on Conditional Formatting, Custom Application of Conditional Formatting, Conditional Formatting Hacks
    • Writing Functions in Python - Best Practices,Context Managers,Decorators,More on Decorators
    • SQL Server Functions for Manipulating Data - Choosing the appropriate data type,Manipulating time,Working With Strings,Recognizing Numeric Data Properties
    • Unit Testing for Data Science in Python - Unit testing basics,Intermediate unit testing,Test Organization and Execution,Testing Models, Plots and Much More
    • Preparing for Coding Interview Questions in Python - Python Data Structures and String Manipulation,Iterable objects and representatives,Functions and lambda expressions,Python for scientific computing
    • Deep Learning with Keras in Python - Introducing Keras, Going Deeper, Improving Your Model Performance, Advanced Model Architectures
    • Experimental Design in Python - The Basics of Statistical Hypothesis Testing, Design Considerations in Experimental Design,Sample size, Power analysis, and Effect size,Testing Normality: Parametric and Non-parametric Tests
    • Building and Optimizing Triggers in SQL Server - Introduction to Triggers,Classification of Triggers,Trigger Limitations and Use Cases,Trigger Optimization and Management
    • Hierarchical and Recursive Queries in SQL Server - Recursion and Common Table Expression (CTE),Hierarchical and Recursive Queries,Creating Data Models on Your Own,Hierarchical Queries of Real-World Examples
    • Winning a Kaggle Competition in Python - Kaggle competitions process,Dive into the Competition,Feature Engineering,Modeling
    • Command Line Automation in Python - IPython shell commands, Shell commands with subprocess,Walking the file system,Command line functions
    • Image Processing in Python - Introducing Image Processing and scikit-image,Filters, Contrast, Transformation and Morphology,Image restoration, Noise, Segmentation and Contours,Advanced Operations, Detecting Faces and Features
    • PostgreSQL Functions for Manipulating Data - Overview of Common Data Types,Working with DATE/TIME Functions and Operators,Parsing and Manipulating Text,Full-text Search and PostgresSQL Extensions
    • Time Series Analysis in SQL Server - Working with Dates and Times,Converting to Dates and Times,Aggregating Time Series Data,Answering Time Series Questions with Window Functions
    • Introduction to AWS Boto in Python - Putting Files in the Cloud, Sharing Files Securely, Reporting and Notifying!, Pattern Rekognition
    • Transactions and Error Handling in SQL Server - Starting with error handling,Raising, throwing and customizing your errors,Transactions in SQL Server,Controlling the concurrency: Transaction isolation levels
    • Data Manipulation with dplyr in R - Transforming Data with dplyr, Aggregating Data, Selecting and Transforming Data, Case Study: The babynames Dataset
    • Introduction to Function Writing in R - How to write a function,All about arguments,Return values and scope,Case study on grain yields
    • Introduction to Data Visualization with ggplot2 - Aesthetics,Geometries,Themes
  • Importing and Cleaning Data
    • Importing Data in Python(Part 2) - Importing data from the Internet,Interacting with APIs to import data from the web,Diving deep into the Twitter API
    • Importing Data in Python(Part 1) - Introduction and flat files,Importing data from other file types,Working with relational databases in Python
    • Cleaning Data in Python - Exploring your data, Tidying data for analysis, Combining data for analysis, Cleaning data for analysis
    • Working with Web Data in R - Downloading Files and Using API Clients,Using httr to interact with APIs directly,Handling JSON and XML,Web scraping with XPATHs,CSS Web Scraping
    • Dealing With Missing Data in R - Why care about missing data?, Wrangling and tidying up missing values,Testing missing relationships,Connecting the dots (Imputation)
    • Cleaning Data with Apache Spark in Python - DataFrame details,Manipulating DataFrames in the real wold,Improving Performance,Complex processing and data pipelines
    • Streamlined Data Ingestion with pandas - Importing Data from Flat Files,Importing Data From Excel Files,Importing Data from Databases,Importing JSON Data and Working with APIs
    • Cleaning Data in R - Introduction and exploring raw data,Tidying data,Preparing data for analysis
    • Importing Data in R (Part 1) - Importing data from flat files with utils,readr , data.table, Importing Excel data, Reproducible Excel work with XLConnect
    • Importing Data in R (Part 2) - Importing data from databases (Part 1), Importing data from databases (Part 2), Importing data from the web (Part 1),Importing data from the web (Part 2), Importing data from statistical software packages
  • Data Manipulation
    • Introduction To Databases in Python - Basics of Relational Databases,Applying Filtering, Ordering and Grouping to Queries, Advanced SQLAlchemy Queries,Creating and Manipulating your own Databases,Putting it all together
    • Manipulating Time Series Data in R with xts & zoo - Introduction to eXtensible Time Series, using xts and zoo for time series,First Order of Business - Basic Manipulations,Merging and modifying time series,Apply and aggregate by time,Extra features of xts
    • pandas Foundations - Data ingestion & inspection,Exploratory data analysis,Time series in pandas,Case Study - Sunlight in Austin
    • Manipulating Dataframes with Pandas - Extracting and transforming data,Advanced indexing,Rearranging and reshaping data,Grouping data,Bringing it all together
    • Merging Dataframes with Pandas - Preparing data,Concatenating data,Merging data,Case Study - Summer Olympics
    • Intro to SQL for data science - Selecting columns,Filtering rows,Aggregate Functions,Sorting, grouping and joins
    • Manipulating Time Series Data in Python - Working with Time Series in Pandas,Basic Time Series Metrics & Resampling,Window Functions: Rolling & Expanding Metrics, Putting it all together: Building a value-weighted index
    • Parallel Computing With Dask - Working with Big Data,Working with Dask Arrays,Working with Dask DataFrames,Working with Dask Bags for Unstructured Data,Case Study: Analyzing Flight Delays
    • Joining Data In SQL - Introduction to joins,Outer joins and cross joins, Set theory clauses, Subqueries
    • Introduction to MongoDB in Python - Flexibly Structured Data,Working with Distinct Values and Sets,Get Only What You Need, and Fast, Aggregation Pipelines: Let the Server Do It For You
    • Joining Data in R with data.table - Joining Multiple data.tables, Joins Using data.table Syntax, Diagnosing and Fixing Common Join Problems,Concatenating and Reshaping data.tables
    • Working with Data in the Tidyverse - Explore your data,Tame your data,Tidy your data,Transform your data
    • Feature Engineering with PySpark - Exploratory Data Analysis,Wrangling with Spark Functions,Feature Engineering,Building a Model
    • Categorical Data in the Tidyverse - Introduction to Factor Variables,Manipulating Factor Variables,Creating Factor Variables,Case Study on Flight Etiquette
    • Pivot Tables with Spreadsheets - Introduction to Pivot Tables for Google Sheets,Behind the Scenes of the Pivot Table,Advanced Options in Pivot Tables,Editing Data and Troubleshooting
    • Working with Geospatial Data in Python - Introduction to geospatial vector data,Spatial relationships,Projecting and transforming geometries,Putting it all together - Artisanal mining sites case study
    • Time Series with data.table in R - Review of data.table,Getting time series data into data.table,Generating lags, differences, and windowed aggregations, Case study: financial data
    • Advanced NLP with spaCy - Finding words, phrases, names and concepts, Large-scale data analysis with spaCy, Processing Pipelines,Training a neural network model
    • Analyzing Social Media Data in Python - Basics of Analyzing Twitter Data, Processing Twitter text,Twitter Networks, Putting Twitter data on the map
    • SQL for Exploratory Data Analysis - What's in the database?, Summarizing and aggregating numeric data, Exploring categorical data and unstructured text,Working with dates and timestamps
    • Intermediate SQL - We'll take the CASE, Short and Simple Subqueries, Correlated Queries, Nested Queries, and Common Table Expressions, Window Functions
    • Introduction to Text Analysis in R - Wrangling Text, Visualizing Text, Sentiment Analysis,Topic Modeling
    • Introduction to Spark SQL with Python - Pyspark SQL,Using window function sql for natural language processing, Caching, Logging, and the Spark UI,Text classification
    • Conditional Formatting in Spreadsheets - A Primer on Conditional Formatting, Custom Application of Conditional Formatting, Conditional Formatting Hacks
    • Improving Query Performance in SQL Server - Introduction, Review and The Order of Things, Filtering and Data Interrogation,Sub-queries and presence or absence,Query performance tuning
    • SQL Server Functions for Manipulating Data - Choosing the appropriate data type,Manipulating time,Working With Strings,Recognizing Numeric Data Properties
    • Analyzing IoT Data in Python - Accessing IoT Data,Processing IoT data,Analyzing IoT data,Machine learning for IoT
    • Improving Query Performance in PostgreSQL - Bringing Together the Data, Minimizing Results and Decreasing the Load, Using Database Designed Properties,Assessing Query Performance
    • Building and Optimizing Triggers in SQL Server - Introduction to Triggers,Classification of Triggers,Trigger Limitations and Use Cases,Trigger Optimization and Management
    • PostgreSQL Functions for Manipulating Data - Overview of Common Data Types,Working with DATE/TIME Functions and Operators,Parsing and Manipulating Text,Full-text Search and PostgresSQL Extensions
    • Regular Expressions in Python - Basic Concepts of String Manipulation,Formatting Strings,Regular Expressions for Pattern Matching,Advanced Regular Expression Concepts
    • Transactions and Error Handling in SQL Server - Starting with error handling,Raising, throwing and customizing your errors,Transactions in SQL Server,Controlling the concurrency: Transaction isolation levels
    • Data Manipulation with dplyr in R - Transforming Data with dplyr, Aggregating Data, Selecting and Transforming Data, Case Study: The babynames Dataset
  • Data Visualization
    • Data Visualization with ggplot2 (Part 1) - Introduction,Data,Aesthetics,Geometries,qplot and wrap-up
    • Data Visualization with ggplot2 (Part 2) - Statistics,Coordinates and Facets,Themes,Best Practices,Case Study
    • Data Visualization with ggplot2 (Part 3) - Statistical plots,Plots for specific data types (Part 1),Plots for specific data types (Part 2),ggplot2 Internals,Data Munging and Visualization Case Study
    • Interactive Data Visualization with Bokeh - Basic plotting with Bokeh,Layouts, Interactions, and Annotations,Building interactive apps with Bokeh,Putting It All Together! A Case Study
    • Data visualization in R - A quick introduction to base R graphics,Different plot types,Adding details to plots,How much is too much?,Advanced plot customization and beyond
    • Introduction to Data Visualization with Python - Customizing plots,Plotting 2D arrays,Statistical plots with Seaborn,Analyzing time series and images
    • Working with geospatial data in R - Basic mapping with ggplot2 and ggmap,Point and polygon data, Raster data and color, Data import and projections
    • Visualizing Time Series Data in R - R Time Series Visualization Tools, Univariate Time Series, Multivariate Time Series,Case study: Visually selecting a stock that improves your existing portfolio
    • Data Visualization in R with lattice - Basic plotting with lattice,Conditioning and the formula interface,Controlling scales and graphical parameters, Customizing plots using panel functions, Extensions and the lattice ecosystem
    • Visualizing Time Series Data in Python - Introduction, Summary Statistics and Diagnostics, Seasonality, Trend and Noise,Work with Multiple Time Series, Case Study
    • Interactive Data Visualization with plotly in R - Introduction to plotly,Styling and customizing your graphics,Advanced charts,Case Study
    • Visualizing Big Data with Trelliscope - General strategies for visualizing big data,ggplot2 + TrelliscopeJS,Trelliscope in the Tidyverse,Case Study: Exploring Montreal BIXI Bike Data
    • Communicating with Data in the Tidyverse - Custom ggplot2 themes,Creating a custom and unique visualization,Introduction to RMarkdown,Customizing your RMarkdown report
    • Visualization Best Practices in R - Proportions of a whole,Point data,Single distributions,Comparing distributions
    • Interactive Maps with leaflet in R - Setting Up Interactive Web Maps,Plotting Points,Groups, Layers, and Extras,Plotting Polygons
    • Data Visualization with Seaborn - Seaborn Introduction,Customizing Seaborn Plots,Additional Plot Types,Creating Plots on Data Aware Grids
    • Visualizing Geospatial Data in Python - Building 2-layer maps : combining polygons and scatterplots,Creating and joining GeoDataFrames,GeoSeries and folium,Creating a choropleth building permit density in Nashville
    • Interactive Data Visualization with rbokeh - rbokeh Introduction,rbokeh Aesthetic Attributes and Figure Options,Data Manipulation for Visualization and More rbokeh Layers,Grid Plots and Maps
    • Intermediate Interactive Data Visualization with plotly in R - Introduction and review of plotly,Animating graphics,Linking graphics,Case Study: Space launches
    • Data Visualization in Spreadsheets - Business Intelligence and Using Dashboards,Efficient Column Charts,Dashboard Controls,Other Charts for Your Dashboard,Conditional Formatting
    • Introduction to Matplotlib - Plotting time-series,Quantitative comparisons and statistical visualizations, Sharing visualizations with others
    • Improving Your Data Visualizations in Python - Highlighting your data,Using color in your visualizations,Showing uncertainty,Visualization in the data science workflow
    • Introduction to Seaborn - Visualizing Two Quantitative Variables,Visualizing a Categorical and a Quantitative Variable,Customizing Seaborn Plots
    • Image Processing in Python - Introducing Image Processing and scikit-image,Filters, Contrast, Transformation and Morphology,Image restoration, Noise, Segmentation and Contours,Advanced Operations, Detecting Faces and Features
    • Introduction to Data Visualization with ggplot2 - Aesthetics,Geometries,Themes
  • Probability and Statistics
    • Statistical thinking in Python - Part 2 - Parameter estimation by optimization,Bootstrap confidence intervals,Introduction to hypothesis testing, Hypothesis test examples
    • Introduction to Time Series Analysis - Exploratory time series data analysis,Predicting the future,Correlation analysis and the autocorrelation function,Autoregression,A simple moving average
    • Statistical Modeling in R(Part 1) - What is statistical modeling,Designing, training, and evaluating models, Assessing prediction performance,Exploring data with models, Covariates and effect size
    • Statistical Modeling in R(Part 2) - Effect size and interaction,Total and partial change,Sampling variability and mathematical transforms,Variables working together
    • Foundations of Inference - Introduction to ideas of inference,Completing a randomization test: gender discrimination,Hypothesis testing errors: opportunity cost,Confidence intervals
    • Exploratory Data Analysis - Exploring Categorical Data,Exploring Numerical Data,Numerical Summaries,Case Study
    • Correlation and Regression - Visualizing two variables,Correlation,Simple linear regression,Interpreting regression models,Model Fit
    • Introduction to Data - Language of data,Study types and cautionary tales,Sampling strategies and experimental design
    • ARIMA Modeling with R - Time Series Data and Models,Fitting ARMA models,ARIMA Models,Seasonal ARIMA
    • Network Analysis in Python(Part 1) - Introduction to networks,Important nodes,Structures
    • Sentiment Analysis in R - Fast & dirty: Polarity scoring, Sentiment analysis the tidytext way, Visualizing sentiment, Case study: Airbnb reviews
    • Foundations of probability in R - The binomial distribution, Laws of probability, Bayesian statistics, Related distributions
    • Forecasting using R - Exploring and visualizing time series in R, Benchmark methods and forecast accuracy, Exponential smoothing, Forecasting with ARIMA models, Advanced methods
    • Spatial Statistics in R - Introduction,Point Pattern Analysis, Areal Statistics, Geostatistics
    • Sentiment Analysis in R: The Tidy Way - Tweets across the United States, Shakespeare gets Sentimental,Analyzing TV News,Singing a Happy Song (or Sad?!)
    • Network Analysis in Python(Part 2) - Bipartite graphs & product recommendation systems, Graph projections, Comparing graphs & time-dynamic graphs
    • Multiple and logistic Regression - Parallel Slopes, Evaluating and extending parallel slopes model, Multiple Regression, Logistic Regression,Case Study: Italian restaurants in NYC
    • Inference for Linear Regression - Inferential ideas, Simulation-based inference for the slope parameter,t-Based Inference For the Slope Parameter,Technical Conditions in linear regression, Building on Inference in Simple Linear Regression
    • Introduction to Time Series Analysis In Python - Correlation and Autocorrelation,Some Simple Time Series,Autoregressive (AR) Models,Moving Average (MA) and ARMA Models
    • Spatial Analysis in R with sf and Raster - Vector and Raster Spatial Data in R,Preparing layers for spatial analysis,Conducting spatial analysis with the sf and raster packages, Combine your new skills into a mini-analysis
    • Network Analysis in R - Introduction to networks, Identifying important vertices in a network, Characterizing network structures,Identifying special relationships
    • Case Studies In Statistical Thinking - Fish sleep and bacteria growth: A review of Statistical Thinking I and II,Analysis of results of the 2015 FINA World Swimming Championships,The "Current Controversy" of the 2013 World Championships,Statistical seismology and the Parkfield region,Earthquakes and oil mining in Oklahoma
    • Inference for Numerical Data - Bootstrapping for estimating a parameter,Introducing the t-distribution,Inference for difference in two parameters,Comparing many means
    • Fundamentals of Bayesian Data Analysis in R - What is Bayesian Data Analysis, How does Bayesian inference work, Why use Bayesian Data Analysis, Bayesian inference with Bayes' theorem, More parameters, more data, and more Bayes
    • Inference for Categorical Data - Inference for a single parameter,Proportions: testing and power,Comparing many parameters: independence,Comparing many parameters: goodness of fit
    • Introduction to Linear Modeling in Python - Exploring Linear Trends, Building Linear Models, Making Model Predictions, Estimating Model Parameters
    • Business Process Analytics in R - Introduction to process analysis, Analysis techniques, Event data processing, Case study
    • Forecasting Product Demand in R - Forecasting demand with time series,Components of demand,Blending regression with time series,Hierarchical forecasting
    • Marketing Analytics in R Statistical Modeling - Modeling Customer Lifetime Value with Linear Regression,Logistic Regression for Churn Prevention,Modeling Time to Reorder with Survival Analysis,Reducing Dimensionality with Principal Component Analysis
    • Network Analysis in RCase Studies - Exploring graphs through time,How do people talk about R on Twitter, Other ways to visualize graph data using ggnet, ggnetwork, and ggiraph
    • Hierarchical and Mixed Effects Models - Overview and introduction to hierarchical and mixed models,Linear mixed-effect models,Generalized linear mixed-effect models,Repeated Measures
    • Customer Analytics & A/B Testing in Python - Key Performance Indicators: Measuring Business Success,Exploring and Visualizing Customer Behavior,The Design and Application of A/B Testing,Analyzing A/B Testing Results
    • Nonlinear Modeling in R with GAMs - Introduction to Generalized Additive Models,Interpreting and Visualizing GAMs,Spatial GAMs and Interactions,Logistic GAMs for Classification
    • Bayesian Modeling with RJAGS - Introduction to Bayesian Modeling,Bayesian Models and Markov Chains, Bayesian Inference & Prediction, Multivariate & Generalized Linear Models
    • Structural Equation Modeling with lavaan in R - One-Factor Models,Multi-Factor Models,Troubleshooting Model Errors and Diagrams,Full Example and an Extension
    • Factor Analysis in R - Evaluating your measure with factor analysis,Multidimensional EFA,Confirmatory Factor Analysis,Refining your measure and/or model
    • Marketing Analytics in R Choice Modeling - Quickstart Guide, Managing and Summarizing Choice Data, Building Choice Models, Hierarchical Choice Models
    • Multivariate Probability Distributions in R - Reading and plotting multivariate data, Multivariate Normal Distribution,Other Multivariate Distributions,Principal Component Analysis and Multidimensional Scaling
    • Experimental Design in R - Introduction to Experimental Design,Basic Experiments,Randomized Complete (& Balanced Incomplete) Block Designs,Latin Squares, Graeco-Latin Squares, & Factorial experiments
    • Building Response Models in R - Response models for aggregate data, Extended sales-response modeling, Response models for individual-level data, Extended choice modeling
    • Bayesian Regression Modeling with rstanarm - Introduction to Bayesian Linear Models, Modifying a Bayesian Model, Assessing Model Fit, Presenting and Using a Bayesian Regression
    • Analyzing Survey Data in R - Introduction to survey data, Exploring categorical data, Exploring quantitative data, Modeling quantitative data
    • Mixture Models in R - Introduction to Mixture Models, Structure of Mixture Models and Parameters Estimation, Mixture of Gaussians with flexmix,Mixture Models Beyond Gaussians
    • A/B Testing in R - Mini case study in A/B Testing, Experimental Design in A/B Testing,Statistical Analyses in A/B Testing
    • Anomaly Detection in R - Statistical outlier detection, Distance and density based anomaly detection, Isolation forest, Comparing performance
    • Statistical Simulation in Python - Basics of randomness and simulation, Probability and data generation process, Resampling methods, Advanced Applications of Simulation
    • Survival Analysis in R - What is Survival Analysis, Estimation of survival curves, The Weibull model, The Cox Model
    • Predictive Analytics using Networked Data in R - Introduction, networks and labelled networks, Homophily, Network Featurization
    • Linear Algebra for Data Science in R - Introduction to Linear Algebra, Matrix-Vector Equations, Eigenvalues and Eigenvectors, Principal Component Analysis
    • Generalized Linear Models in R - GLMs, an extension of your regression toolbox, Logistic Regression, Interpreting and visualizing GLMs, Multiple regression with GLMs
    • Longitudinal Analysis in R - Longitudinal Data, Modeling Continuous Longitudinal Outcomes, Add fixed predictor variables, Modeling Longitudinal Dichotomous Outcomes
    • Statistics in Spreadsheets - Getting To Know Your Data, Statistical Data Visualization, Statistical Hypothesis Testing,Case Study: Dating Profile Analysis
    • Foundations of Probability in Python - Calculate some probabilities,Important probability distributions,Probability meets statistics
    • Preparing for Statistics Interview Questions in Python - Probability and Sampling Distributions,Exploratory Data Analysis,Statistical Experiments and Significance Testing,Regression and Classification
    • Experimental Design in Python - The Basics of Statistical Hypothesis Testing, Design Considerations in Experimental Design,Sample size, Power analysis, and Effect size,Testing Normality: Parametric and Non-parametric Tests
    • Time Series Analysis in SQL Server - Working with Dates and Times,Converting to Dates and Times,Aggregating Time Series Data,Answering Time Series Questions with Window Functions
    • Survey and Measurement Development in R - Preparing to analyze survey data,Exploratory factor analysis & survey development,Confirmatory factor analysis & construct validation,Criterion validity & replication
  • Machine Learning
    • Deep Learning in Python - Basics of deep learning and neural networks, Optimizing a neural network with backward propagation, Building deep learning models with keras, Fine-tuning keras models
    • Natural Language Processing Fundamentals in Python - Regular expressions , word tokenization, Simple topic identification, Named-entity recognition, Building a "fake news" classifier
    • Introduction to Machine Learning - What is Machine Learning, Performance measures, Classification, Regression, Clustering
    • Machine Learning Toolbox - Regression models: fitting them and evaluating their performance,Classification models: fitting them and evaluating their performance,Tuning model parameters to improve performance,Preprocessing your data,Selecting models: a case study in churn prediction
    • Unsupervised Learning in R - Hierarchical clustering,Dimensionality reduction with PCA,Putting it all together with a case study
    • Supervised Learning with scikit-learn - Classification, Regression, Fine-tuning your model,Preprocessing and pipelines
    • Unsupervised Learning in Python - Clustering for dataset exploration, Visualization with hierarchical clustering and t-SNE, Decorrelating your data and dimension reduction, Discovering interpretable features
    • Supervised Learning in R: Classification - k-Nearest Neighbors (kNN), Naive Bayes, Logistic Regression,Classification Trees
    • Machine Learning With Tree-Based Models in R - Classification Trees, Regression Trees, Bagged Trees, Random Forests, Boosted Tree
    • Building chatbots in Python - Chatbots 101, Understanding natural language,Building a virtual assistant,Dialogue
    • Extreme Gradient Boosting with XGBoost - Classification with XGBoost,Regression with XGBoost,Fine-tuning your XGBoost model ,Using XGBoost in pipelines
    • Supervised Learning in R: Regression - What is Regression?, Training and Evaluating Regression Models, Issues to Consider, Dealing with Non-Linear Responses, Tree-Based Methods
    • Dimensionality Reduction in R - Principal component analysis (PCA), Advanced PCA & Non-negative matrix factorization (NNMF),Exploratory factor analysis (EFA),Advanced EFA
    • Cluster Analysis in R - Calculating distance between observations, Hierarchical clustering, K-means clustering
    • Modeling with Data in the Tidyverse - Introduction to Modeling, Modeling with Basic Regression, Modeling with Multiple Regression, Model Assessment and Selection
    • Supervised Learning in R: Case Studies - Not mtcars AGAIN,Stack Overflow Developer Survey, Get out the vote, But what do the nuns think?
    • Machine Learning For Time Series Data In Python - Time Series and Machine Learning Primer, Time Series as Inputs to a Model, Predicting Time Series Data, Validating and Inspecting Time Series Models
    • Linear Classifiers in Python - Applying logistic regression and SVM, Loss functions, Logistic regression, Support Vector Machines
    • HR Analytics in Python: Predicting Employee Churn - Introduction to HR Analytics, Predicting employee turnover with decision trees, Evaluating the turnover prediction model, Choosing the best turnover prediction model
    • Machine Learning with Tree-Based Models in Python - Classification and Regression Trees, The Bias-Variance Tradeoff, Bagging and Random Forests, Boosting, Model Tuning
    • Foundations of Predictive Analytics in Python (Part 1) - Building Logistic Regression Models, Forward stepwise variable selection for logistic regression, Explaining model performance to business, Interpreting and explaining models
    • Advanced Deep Learning with Keras in Python - The Keras Functional API, Two Input Networks Using Categorical Embeddings, Shared Layers, and Merge Layers, Multiple Inputs: 3 Inputs (and Beyond!), Multiple Outputs
    • Preprocessing for Machine Learning in Python - Introduction to Data Preprocessing, Standardizing Data, Feature Engineering, Selecting features for modeling
    • Machine Learning for Finance in Python - Preparing data and a linear model, Machine learning tree methods, Neural networks and KNN, Machine learning with modern portfolio theory
    • Hyperparameter Tuning in R - Introduction to hyperparameters, Hyperparameter tuning with caret, Hyperparameter tuning with mlr, Hyperparameter tuning with h2o
    • Support Vector Machines in R - Support Vector Classifiers - Linear Kernels, Polynomial Kernels, Radial Basis Function Kernels
    • Building Recommendation Engines with PySpark - Recommendations Are Everywhere, How does Alternating Least Squares algorithm work, Recommending Movies,What if you don't have customer ratings?
    • Machine Learning in the Tidyverse - Foundations of "tidy" Machine learning, Multiple Models with broom package, Build, Tune & Evaluate Regression Models, Build, Tune & Evaluate Classification Models
    • Convolutional Neural Networks for Image Processing - Image Processing With Neural Networks, Using Convolutions, Going Deeper, Understanding and Improving Deep Convolutional Networks
    • Supply Chain Analytics in Python - Basics of Supply Chain Optimization and PuLP, Modeling in PuLP, Solve and Evaluate Model, Sensitivity and Simulation Testing of Model
    • Fraud Detection in R - Social network analytics, Imbalanced class distributions , Digit analysis and robust statistics
    • Fraud Detection in Python - Introduction and preparing your data, Fraud detection using labelled data, Fraud detection using unlabelled data, Fraud detection using text
    • Advanced Dimensionality Reduction in R - Introduction to Advanced Dimensionality Reduction, Introduction to t-SNE, Using t-SNE with Predictive Models, Generalized Low Rank Models (GLRM)
    • Customer Segmentation in Python - Cohort Analysis, Recency, Frequency, Monetary Value analysis, Data pre-processing for clustering, Customer Segmentation with K-means
    • Topic Modeling in R - Quick introduction to the workflow, Wordclouds, stopwords, and control arguments, Named entity recognition as unsupervised classification, How many topics is enough?
    • Feature Engineering in R - Creating Features from Categorical Data, Creating Features from Numeric Data, Transforming Numerical Features, Advanced Methods
    • Foundations of Predictive Analytics in Python (Part 2) - Crucial base table concepts, Creating variables, Data preparation, Advanced base table concepts
    • Big Data Fundamentals via PySpark - Introduction to Big Data analysis with Spark,Programming in PySpark RDD’s,PySpark SQL & DataFrames,Machine Learning with PySpark MLlib
    • Dimensionality Reduction in Python - Exploring high dimensional data, Feature selection I, selecting for feature information,Feature selection II, selecting for model accuracy,Feature extraction
    • Designing Machine Learning Workflows in Python - The Standard Workflow,The Human in the Loop,Model Lifecycle Management,Unsupervised Workflows
    • Feature Engineering for Machine Learning in Python - Creating Features, Dealing with Messy Data, Conforming to Statistical Assumptions,Dealing with Text Data
    • Clustering Methods with SciPy - Introduction to Clustering,Hierarchical Clustering,K-Means Clustering,Clustering in Real World
    • Machine Learning with Apache Spark - Introduction,Classification,Regression,Ensembles & Pipelines
    • Introduction to TensorFlow in Python - Introduction to TensorFlow,Linear Regression in TensorFlow,Neural Networks in TensorFlow,High Level APIs in TensorFlow
    • Model Validation in Python - Basic Modeling in scikit-learn,Validation Basics,Cross Validation,Selecting the best model with Hyperparameter tuning.
    • Hyperparameter Tuning in Python - Hyperparameters and Parameters,Grid search,Random Search,Informed Search
    • Ensemble Methods in Python - Combining Multiple Models, Bagging, Boosting, Stacking
    • Generalized Linear Models in Python - Introduction to GLMs, Modeling Binary Data, Modeling Count Data, Multivariable Logistic Regression
    • Deep Learning with PyTorch - Introduction to PyTorch,Artificial Neural Networks,Convolutional Neural Networks (CNNs),Using Convolutional Neural Networks
    • Deep Learning with Keras in Python - Introducing Keras, Going Deeper, Improving Your Model Performance, Advanced Model Architectures
    • Feature Engineering for NLP in Python - Basic features and readability scores,Text preprocessing, POS tagging and NER,N-Gram models,TF-IDF and similarity scores
    • Winning a Kaggle Competition in Python - Kaggle competitions process,Dive into the Competition,Feature Engineering,Modeling
    • Sentiment Analysis in Python - Sentiment Analysis Nuts and Bolts,Numeric Features from Reviews,More on Numeric Vectors: Transforming Tweets,Let's Predict the Sentiment
    • Forecasting Using ARIMA Models in Python - ARMA Models,Fitting the Future,The Best of the Best Models,Seasonal ARIMA Models
  • Reporting
    • Building Web Applications in R with Shiny - Shiny review, Make the perfect plot using Shiny, Explore a dataset interactively with Shiny, Create your own word cloud in Shiny
    • Building Dashboards with flexdashboard - Dashboard Layouts, Data Visualization for Dashboards, Dashboard Components, Adding Interactivity with Shiny
    • Building dashboards with shinydashboard - Building Static Dashboards, Building Dynamic Dashboards, Customizing Style
    • Reporting in SQL - Exploring the Olympics Dataset, Creating Reports, Cleaning & Validation, Complex Calculations
    • Data-Driven Decision Making in SQL - Introduction to business intelligence ,Decision Making with simple SQL queries, Data Driven Decision Making with advanced SQL queries, Data Driven Decision Making with OLAP SQL queries
    • Hierarchical and Recursive Queries in SQL Server - Recursion and Common Table Expression (CTE),Hierarchical and Recursive Queries,Creating Data Models on Your Own,Hierarchical Queries of Real-World Examples

Does this data science training offer certification?

No – because it's useless . Employers would much rather see work you've done—than a piece of paper that says you have the ability to do work. In my experience most of these certificates are a complete joke. Building a portfolio showcasing your projects works better . No one hiring for a (good) data scientist job takes these certifications seriously, because they are a poor substitute for examples.

Does this data science course teach big data?

Yes it does. High level concepts of Hadoop framework are definitely discussed. I understand that not all datasets that you will use in your projects will be small. Few datasets are so large that they cannot be processed by the legacy technologies. You will need the latest big data technologies like PySpark to process them.

Hence, it gives you a coding exercise to build a data pipeline(an ideal mix of software frameworks that automate the management, analysis and visualization of data from multiple sources) using Spark.

Data Science job roles that this program helps you to get into

Below are the different data related roles that you may apply - post completing the full course.

Job TitleRole
Data Analystcollects, processes and performs statistical data analysis
Data Architectcreates blueprints for data management systems to integrate, centralize, protect and maintain data sources
Data Engineerdevelops, constructs, tests, and maintains architectures
Statisticiancollects, analyzes and interprets qualitative and quantitative data with statistical theories and methods
Business Analystimproves business process as an intermediary between business and IT
Data and Analytics Managermanages a team of analysts and data scientists

How is this data science training helpful for novice learners ?

The main advantage is that you will focus on practical application from day one. We will not spend lot of time in discussing theoretical concepts. Rather we will use that time to do live coding . As a first step, we will discuss the Python language basics. Then we shall focus on the Numpy and Pandas packages. These two packages are very important for newbie learners. We will also focus on specific concepts like Boolean indexing, and processing dataframes in chunks.

You will then proceed to do data cleaning and data visualization. These topics are mandatory for every aspiring data scientist . Post this, we shall discuss command line scripting and GitHub – a version control system for the code. Then you will learn about Web Scraping and the API’s used to do it. For each of the topic, you will get assignments to work on. Going further, we will discuss statistics concepts and the R language. I think this is the only data science course in Hyderabad that has such in-depth content! Along with these, you will get a strong foundation in these topics – Probability, Calculus, Linear Algebra , Linear Regression, Machine Learning, Deep Learning etc.

Data Science Course Calendar

Batch Start DateTimingsPriceMode
17-Dec-20197AM to 9AMRs.40000Online Training
17-Jan-20197AM to 9AMRs.35000Online Training
17-Feb-20207AM to 9AMRs.30000Online Training
17-Mar-20207AM to 9AMRs.25000Online Training

Data Science Course Testimonials

Good class, intense, and gets you exposed to a lot of Data Science features and idiosyncrasies. The exercises are well organized, typically you can do them as long as you're paying attention even if you haven't mastered the syntax yet or fully grasped the concept. We will be using this class as part of our company standard Data Science training

Swapna A

Interesting course to do. Good start in the Data Science . I am also very satisfied with the material and the technical environment of this data science course. The teacher's knowledge is excellent and very approachable for questions.

Kishore K

A fantastic course. I found the structure and content of the course ideal for learning Data Science and Machine Learning. Having a real live instructor was a huge benefit especially when completing the exercises so I could get instant feedback and ask questions. I loved the format and delivery it was extremely effective. I also really liked how the course dives straight into Python and R code from the ground up and avoids the usual longwinded history of these programming languages. I definitely recommend this course to anyone looking to learn Data Science in Hyderabad. Excellent!

Ananya V

The Instructor was very knowledgeable, and the class structure was well prepared and structured to maximize learning. Having the instructor online vs. a recorded video is a great advantage. I would recommend this data science course to any developer. I hope that the trainer offers advanced deep learning classes in the same format.

Srinivas S

Great training on data science. Would recommend this course to not only people new to data science but also to people who want to strengthen their existing knowledge. There are other institutes in Hyderabad that cover Data Science and will only introduce the core concepts when necessary. The training is different in that it covers the core data science concepts, gives examples and does it well right from the word "GO"

Sandeep B

Different from other online classes, this course is fully interactive. The instructor teaches the data science course through conference call, and the course is so interactive that the instructor can know your code in real time. You can ask questions like the traditional course taught in classroom

Karthik K

Since the course takes pragmatic approach, it is packed with case studies and capstone projects.The feedback of your coding is real time from both editor and the instructor. If you like learning by doing, this is the right course for you. I benefited from this approach a lot from this data science training.

Supriya G

I was surprised by the concise and fast pace of the course sessions. The practical aspect of the course helps in learning the concepts and applying them very fast.I would recommend it as one of the Best Data science training institute in Hyderabad.I have all what I need to start attending data scientist interviews as well

Raghu C

Few online resources worth studying

While there is a lot of material in the web, there are only a few resources that are worth your time.

  • The Open Source Data Science Masters – Free collection of open source materials and resources to learn data science.
  • Data Science Fundamentals - This program covers data science 101, methodology, hands-on applications, programming in R and open source tools
  • Learning from Data - This course focuses on machine learning and is delivered as a series of video lectures along with homework assignments and a final exam