My post here is to help you with the details of a course that makes you learn hands 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.
Contents
 1 How is machine learning related to data science?
 2 Programming languages taught in these classes on data science
 3 The objectives & learning outcomes of this training
 4 Course Topic Outlines
 5 Does this training offer certification post completion?
 6 How is this training helpful for novice learners like me?
 7 Data Science Course Calendar
 8 Data Science Course Testimonials
 9 Few online resources worth studying
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.
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
Python  R  SQL 

1.Easiest to learn general purpose programming language.  1.Open source language for statistical computing  1.Nonprocedural language used for querying data stored in RDBMS. 
2.Maintained by the Python Software Foundation  2.Maintained by the R Foundation for Statistical Computing  2.Maintained by the American National Standards Institute(ANSI) 
3.Used in web development and scientific computing  3.Used in statistical model development  3.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 objectives & learning outcomes of this 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 , Multivariate , Algebra, Linear and Quadratic models , Integer Programming , PearsonR, MLlib, Lambda Functions or ChiSquare , Tests for Significance and Standard Deviation
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.
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, variablelength arguments and scope,Lambda functions and errorhandling
 ObjectOriented Programming in R:S3 and R6 – Introduction to ObjectOriented 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
 ObjectOriented Programming in Python – Getting ready for objectoriented 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 tSNE, Using tSNE with Predictive Models, Generalized Low Rank Models (GLRM)
 Customer Segmentation in Python – Cohort Analysis, Recency, Frequency, Monetary Value analysis, Data preprocessing for clustering, Customer Segmentation with Kmeans
 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 timeseries,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,KMeans 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 scikitlearn,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,Subqueries and presence or absence,Query performance tuning
 Analyzing Business Data in SQL – Revenue, cost, and profit, Usercentric 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
 DataDriven 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,NGram models,TFIDF 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 & Nonnegative matrix factorization (NNMF),Exploratory factor analysis (EFA),Advanced EFA
 Inference for Numerical Data – Bootstrapping for estimating a parameter,Introducing the tdistribution,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 Nonparametric 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 RealWorld 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 scikitimage,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,Fulltext 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 valueweighted 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, Largescale 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,Subqueries 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,Fulltext 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 wrapup
 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 2layer 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 timeseries,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 scikitimage,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 & timedynamic 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, Simulationbased inference for the slope parameter,tBased 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 minianalysis
 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 tdistribution,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 mixedeffect models,Generalized linear mixedeffect 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 – OneFactor Models,MultiFactor 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, GraecoLatin Squares, & Factorial experiments
 Building Response Models in R – Response models for aggregate data, Extended salesresponse modeling, Response models for individuallevel 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, MatrixVector 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 Nonparametric 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, Finetuning keras models
 Natural Language Processing Fundamentals in Python – Regular expressions , word tokenization, Simple topic identification, Namedentity 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 scikitlearn – Classification, Regression, Finetuning your model,Preprocessing and pipelines
 Unsupervised Learning in Python – Clustering for dataset exploration, Visualization with hierarchical clustering and tSNE, Decorrelating your data and dimension reduction, Discovering interpretable features
 Supervised Learning in R: Classification – kNearest Neighbors (kNN), Naive Bayes, Logistic Regression,Classification Trees
 Machine Learning With TreeBased 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,Finetuning 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 NonLinear Responses, TreeBased Methods
 Dimensionality Reduction in R – Principal component analysis (PCA), Advanced PCA & Nonnegative matrix factorization (NNMF),Exploratory factor analysis (EFA),Advanced EFA
 Cluster Analysis in R – Calculating distance between observations, Hierarchical clustering, Kmeans 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 TreeBased Models in Python – Classification and Regression Trees, The BiasVariance 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 tSNE, Using tSNE with Predictive Models, Generalized Low Rank Models (GLRM)
 Customer Segmentation in Python – Cohort Analysis, Recency, Frequency, Monetary Value analysis, Data preprocessing for clustering, Customer Segmentation with Kmeans
 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,KMeans 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 scikitlearn,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,NGram models,TFIDF 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
 DataDriven 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 RealWorld Examples