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Data Science analytics, course, course based on R Language and Python in wakad, pune, hinjewadi, aundh, hadapsar

    Data Science and Data Analytics Using R | Python | Statistics and BIgData
    Duration : 8 Weekends 3 Hours on Saturday and Sundays
    Trainer has 11+ Years experience in IT Industry
    Real Time Projects , Assignments , scenarios are part of this course

    Data Science and Data Analytics Introduction (Week-1)

    • What is Data Science
    • Differentiate between Database Datawarehouse Hadoop Bigdata and Data Science
    • Why Data Science is in demand on the top of Hadoop Ecosystem
    • Components in data Science
    • Real time examples and applications of Data Science
    • What is Statistics
    • Introduction to R Language
    • Introduction to R Language and Statistics
    • Statistics in Excel Sheet
    • Introduction to Python Language
    • IQuestions and Answers

    Introduction to R Language (Week-2)

    • Harnessing the power of R
    • Assigning Variables
    • Printing an output
    • Numbers are of type numeric
    • Characters and Dates
    • Logicals

    Arrays, Vectors and Matrices in R Language (Week-3)

    • Creating an Array
    • Indexing an Array
    • Operations between 2 Arrays
    • Operations between an Array and a Vector
    • Outer Products
    • 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)
    • A Matrix is a 2-Dimensional Array
    • Creating a Matrix
    • Matrix Multiplication
    • Merging Matrices
    • Solving a set of linear equations

    Factors, Lists, Data Frames,Regression Quantifies Relationships Between Variables in R Language (Week-4)

    • 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()
    • Introducing Lists
    • Introducing Data Frames
    • Reading Data from files
    • Indexing a Data Frame
    • Aggregating and Sorting a Data Frame
    • Merging Data Frames
    • Introducing Regression
    • What is Linear Regression?
    • A Regression Case Study : The Capital Asset Pricing Model (CAPM)

    Linear Regression and Data Visualization using R and Excel (Week-5)

    • Linear Regression in Excel : Preparing the data
    • Linear Regression in Excel : Using LINEST()
    • Linear Regression in R : Preparing the data
    • Linear Regression in R : lm() and summary()
    • Multiple Linear Regression
    • Adding Categorical Variables to a Linear model
    • Robust Regression in R : rlm()
    • Parsing Regression Diagnostic Plots
    • Data Visualization
    • The plot() function in R
    • Control color palettes with RColorbrewer
    • Drawing barplots
    • Drawing a Heatmap
    • Drawing a Scatterplot Matrix
    • Plot a line chart with ggplot2

    Getting Started With Python and Statistics, Probability Refresher in Python (Week-6)

    • Introduction to Python Language
    • Getting What You Need in Python Library
    • Installation
    • Python language Basics
    • Running Python Scripts
    • 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
    • matplotlib plotting library
    • Covariance and Correlation
    • Conditional Probability
    • Conditional Probability usecases
    • Bayes’ Theorem

    Predictive Models and Machine Learning with Python (Week-7)

    • Linear Regression
    • Polynomial Regression
    • Multivariate Regression, and Predicting Analysis
    • Multi-Level Models
    • 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 Example
    • Measuring Entropy
    • Install GraphViz
    • Decision Trees: Concepts
    • Decision Trees: Predicting Hiring Decisions
    • Ensemble Learning
    • Support Vector Machines (SVM) Overview
    • Using SVM to cluster people using scikit-learn

    Project and Profile Discussion with Mock Interview Session (Week-8)

    • How to work in Real time Project
    • Real time Project Scenarios
    • Frequent Challanges in Projects and solutions
    • Mock Interview session
    • Profile discussion
    • Mock Test
    • Questions and Answers

    Additional Benifits

    • Trainer is Working It Professionals
    • POCs and Material will be provided by Institute
    • Once Registered can come and join multiple batches
    • We also provide Combination of Hadoop and Data Science
You will get Training completion Certification from Institute
  • Trainer is Working IT Professionals
  • POCs and Material will be provided by Institute
  • Once Registered can come and join multiple batches
  • We also provide Combination of Hadoop and Data Science
We start course with Basics of Data Science and Analytics and cover R, Python and Statistics in Depth with Machine Learning.
Trainer has 10+ years Experience in Software Industry
Trainer is IT working professional and having 10+ years experience.

We Start new Batch every Month please call us at 860-099-8107 or email contact@technogeekscs.co.in for latest update about upcoming batches!

Real time project and 2 POCs will be covered as the part of Training