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Data Science

Data Science and Data Analytics Using Spark | R | Python

Learn Data Science, Deep Learning, & Machine Learning with Python & R Language With Live Machine Learning & Deep Learning Projects

Duration : 3 Months – Weekends 3 Hours on Saturday and Sundays

Real Time Projects , Assignments , scenarios are part of this course

Data Sets , installations , Interview Preparations , Repeat the session until 6 months are all attractions of this particular course

Trainer :- Experienced DataScience Consultant

Want to be Future Data Scientist

Introduction: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

Data Science, Statistics with R & Python: This course is an introduction to Data Science and Statistics using the R programming language with Python. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R and Python. If you’re new to Python, don’t worry – the course starts with a crash course. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems.

What’s Spark? If you are an analyst or a data scientist, you’re used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.

Scala: Scala is a general purpose programming language – like Java or C++. It’s functional programming nature and the availability of a REPL environment make it particularly suited for a distributed computing framework like Spark.

Analytics: Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.

Machine Learning and Data Science : Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.

Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.

What am I going to get from this course?

  • Harness R and R packages to read, process and visualize data
  • Understand linear regression and use it confidently to build models
  • Understand the intricacies of all the different data structures in R
  • Use Linear regression in R to overcome the difficulties of LINEST() in Excel
  • Draw inferences from data and support them using tests of significance
  • Use descriptive statistics to perform a quick study of some data and present results
  • Use Spark for a variety of analytics and Machine Learning tasks
  • Understand functional programming constructs in Scala
  • Implement complex algorithms like PageRank or Music Recommendations
  • Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings
  • Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX
  • Write code in Scala REPL environments and build Scala applications with an IDE
  • Course Completion Certificate.

Target audience?

  • Engineering/Management Graduate or Post-graduate Fresher Students who want to make their career in Data Science Industry or want to be future Data Scientist.
  • Engineers who want to use a distributed computing engine for batch or stream processing or both
  • Analysts who want to leverage Spark for analyzing interesting datasets
  • Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
  • MBA Graduates or business professionals who are looking to move to a heavily quantitative role.
  • Engineering Graduate/Professionals who want to understand basic statistics and lay a foundation for a career in Data Science
  • Working Professional or Fresh Graduate who have mostly worked in Descriptive analytics or not work anywhere and want to make the shift to being modelers or data scientists
  • Professionals who’ve worked mostly with tools like Excel and want to learn how to use R for statistical analysis.

Course Curriculum

Getting Started
  • Course Introduction
  • Course Material & Lab Setup
  • Installation
  • Python Basic – Part – 1
  • Python Basic – Part – 2
  • Advance Python – Part – 1
  • Advance Python – Part – 2
Statistics and Probability Refresher, and Python Practice
  • Types of Data
  • Mean, Median, Mode
  • Using mean, median, and mode in Python
  • Variation and Standard Deviation
  • Probability Density Function; Probability Mass Function
  • Common Data Distributions
  • Percentiles and Moments
  • A Crash Course in matplotlib
  • Covariance and Correlation
  • Conditional Probability
  • Exercise Solution: Conditional Probability of Purchase by Age
  • Bayes’ Theorem
Predictive Models
  • Linear Regression
  • Polynomial Regression
  • Multivariate Regression, and Predicting Car Prices
  • Multi-Level Models
Machine Learning with Python
  • Supervised vs. Unsupervised Learning, and Train/Test
  • Using Train/Test to Prevent Overfitting a Polynomial Regression
  • Bayesian Methods: Concepts
  • Implementing a Spam Classifier with Naive Bayes
  • K-Means Clustering
  • Clustering people based on income and age
  • Measuring Entropy
  • Install GraphViz32. Decision Trees: Concepts
  • Decision Trees: Predicting Hiring Decisions
  • Ensemble Learning
  • Support Vector Machines (SVM) Overview
  • Using SVM to cluster people using scikit-learn
Recommender Systems
  • User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
  • Finding Movie Similarities
  • Improving the Results of Movie Similarities
  • Making Movie Recommendations to People
  • Improve the recommender’s results
More Data Mining and Machine Learning Techniques
  • K-Nearest-Neighbors: Concepts
  • Using KNN to predict a rating for a movie
  • Dimensionality Reduction; Principal Component Analysis
  • PCA Example with the Iris data set
  • Data Warehousing Overview: ETL and ELT
  • Reinforcement Learning
Dealing with Real-World Data
  • Bias/Variance Tradeoff
  • K-Fold Cross-Validation to avoid overfitting
  • Data Cleaning and Normalization
  • Cleaning web log data
  • Normalizing numerical data
  • Detecting outliers
Apache Spark: Machine Learning on Big Data
  • Lab Set-up Warning & Error Handling
  • Installing Spark – Part – 1
  • Installing Spark – Part – 2
  • Spark Introduction
  • Spark and the Resilient Distributed Dataset (RDD)
  • Introducing MLLib
  • Decision Trees in Spark
  • K-Means Clustering in Spark
  • TF / IDF
  • Searching Wikipedia with Spark
  • Using the Spark 2.0 DataFrame API for MLLib
Experimental Design
  • A/B Testing Concepts
  • T-Tests and P-Values
  • Hands-on With T-Tests
  • Determining How Long to Run an Experiment
  • A/B Test Gotchas
Deep Learning and Neural Networks
  • Deep Learning Pre-Requisites
  • The History of Artificial Neural Networks
  • Deep Learning in the Tensorflow Playground
  • Deep Learning Details
  • Introducing Tensorflow
  • Using Tensorflow, Part 1
  • Using Tensorflow, Part 2
  • Introducing Keras
  • Using Keras to Predict Political Affiliations
  • Convolutional Neural Networks (CNN’s)
  • Using CNN’s for handwriting recognition
  • Recurrent Neural Networks (RNN’s)
  • Using a RNN for sentiment analysis
  • The Ethics of Deep Learning
  • Learning More about Deep Learning

Statistics and Data Science in R

  • Introduction to R
  • R and R studio Installation & Lab Setup
  • Descriptive Statistics
Descriptive Statistics
  • Mean, Median, Mode
  • Our first foray into R : Frequency Distributions
  • Draw your first plot : A Histogram
  • Computing Mean, Median, Mode in R
  • What is IQR (Inter-quartile Range)?
  • Box and Whisker Plots
  • The Standard Deviation
  • Computing IQR and Standard Deviation in R
Inferential Statistics
  • Drawing inferences from data
  • Random Variables are ubiquitous
  • The Normal Probability Distribution
  • Sampling is like fishing
  • Sample Statistics and Sampling Distributions
Case studies in Inferential Statistics
  • Case Study 1 : Football Players (Estimating Population Mean from a Sample)
  • Case Study 2 : Election Polling (Estimating Population Proportion from a Sample)
  • Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean)
  • Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion)
  • Case Study 5: A/B Testing (Comparing the means of two populations)
  • Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)
Diving into R
  • Harnessing the power of R
  • Assigning Variables
  • Printing an output
  • Numbers are of type numeric
  • Characters and Dates
  • Logicals
  • Data Structures are the building blocks of R
  • Creating a Vector
  • The Mode of a Vector
  • Vectors are Atomic
  • Doing something with each element of a Vector
  • Aggregating Vectors
  • Operations between vectors of the same length
  • Operations between vectors of different length
  • Generating Sequences
  • Using conditions with Vectors
  • Find the lengths of multiple strings using Vectors
  • Generate a complex sequence (using recycling)
  • Vector Indexing (using numbers)
  • Vector Indexing (using conditions)
  • Vector Indexing (using names)
  • Creating an Array
  • Indexing an Array
  • Operations between 2 Arrays
  • Operations between an Array and a Vector
  • Outer Products
  • A Matrix is a 2-Dimensional Array
  • Creating a Matrix
  • Matrix Multiplication
  • Merging Matrices
  • Solving a set of linear equationsv
  • What is a factor?
  • Find the distinct values in a dataset (using factors)
  • Replace the levels of a factor
  • Aggregate factors with table()
  • Aggregate factors with tapply()
Lists and Data Frames
  • Introducing Lists
  • Introducing Data Frames
  • Reading Data from files
  • Indexing a Data Frame
  • Aggregating and Sorting a Data Frame
  • Merging Data Frames
Regression quantifies relationships between variables
  • Linear Regression in Excel : Preparing the data.
  • Linear Regression in Excel : Using LINEST()
Linear Regression in R
  • Linear Regression in R : Preparing the data
  • Linear Regression in R : lm() and summary()
  • Multiple Linear Regression
  • Adding Categorical Variables to a linear mode
  • Robust Regression in R : rlm()
  • Parsing Regression Diagnostic Plots
Data Visualization in R
  • Data Visualization
  • The plot() function in R
  • Control color palettes with RColorbrewer
  • Drawing bar plots
  • Drawing a heatmap
  • Drawing a Scatterplot Matrix
  • Plot a line chart with ggplot