Data Scientist with R Training Cost

Classroom

  • 8-Day(4 weekends) Intensive Program
  • 3 Months Live Project Mentoring
88000
44000

Live Virtual

  • 80 Hrs Live Virtual Intensive Program
  • 3 Months Live Project Mentoring
79000
39000

Self Learning

  • 1 Year Access to Elearning content
  • 3 Months of Live Project Mentoring
44000
22000

About Data Scientist

DATA SCIENCE TRAINING

About Data Scientist

DESCRIPTION

Syllabus

The following topics are covered here

Module 1 - Introduction to Data Science with Python

  • Installing Python Anaconda distribution
  • Python native Data Types
  • Basic programing concepts
  • Python data science packages overview

Module 2 - Python Basics: Basic Syntax, Data Structures

  • Python Objects
  • Math & Comparision Operators
  • Conditional Statement
  • Loops
  • Lists, Tuples, Strings, Dictionaries, Sets
  • Functions
  • Exception Handling

Module 3 - Numppy Package

  • Importing Numpy
  • Numpy overview
  • Numpy Array creation and basic operations
  • Numpy Universal functions
  • Selecting and retrieving Data
  • Data Slicing
  • Iterating Numpy Data
  • Shape Manupilation
  • Stacking and Splitting Arrays
  • Copies and Views : no copy, shallow copy , deep copy
  • Indexing : Arrays of Indices, Boolean Arrays

Module 4 - Pandas Package

  • Importing Pandas
  • Pandas overview
  • Object Creation : Series Object , DataFrame Object
  • View Data
  • Selecting data by Label and Position
  • Data Slicing
  • Boolean Indexing
  • Setting Data

Module 5 - Python Advanced: Data Mugging with Pandas

  • Applying functions to data
  • Histogramming
  • String Methods
  • Merge Data : Concat, Join and Append
  • Grouping & Aggregation
  • Reshaping
  • Analysing Data for missing values
  • Filling missing values: fill with constant, forward filling, mean
  • Removing Duplicates
  • Transforming Data

Module 6 - Python Advanced: Visualization with MatPlotLib

  • Importing MatPlotLib & Seaborn Libraries
  • Creating basic chart : Line Chart, Bar Charts and Pie Charts
  • Ploting from Pandas object
  • Saving a plot
  • Object Oriented Plotting : Setting axes limits and ticks
  • Multiple Plots
  • Plot Formatting : Custom Lines, Markers, Labels, Annotations, Colors
  • Satistical Plots with Seaborn

Module 7 - Exploratory Data Analysis: Case Study

The following topics are covered here

Module 1: Introduction to Statistics

  • Two areas of Statistics in Data Science
  • Applied statistics in business
  • Descriptive Statistics
  • Inferential Statistics
  • Statistics Terms and definitions
  • Type of Data
  • Quantitative vs Qualitative Data
  • Data Measurement Scales

Module 2: Harnessing Data

  • Sampling Data, with and without replacement
  • Sampling Methods, Random vs Non-Random
  • Measurement on Samples
  • Random Sampling methods
  • Simple random, Stratified, Cluster, Systematic sampling.
  • Biased vs unbiased sampling
  • Sampling Error
  • Data Collection methods

Module 3: Exploratory Analysis

  • Measures of Central Tendencies
  • Mean, Median and Mode
  • Data Variability : Range, Quartiles, Standard Deviation
  • Calculating Standard Deviation
  • Z-Score/Standard Score
  • Empirical Rule
  • Calculating Percentiles
  • Outliers

Module 4: Distributions

  • Distribtuions Introduction
  • Normal Distribution
  • Central Limit Theorem
  • Histogram - Normalization
  • Other Distributions: Poisson, Binomial et.,
  • Normality Testing
  • Skewness
  • Kurtosis
  • Measure of Distance
  • Euclidean , Manhattan and Minkowski Distance

Module 5: Hypothesis & computational Techniques

  • Hypothesis Testing
  • Null Hypothesis, P-Value
  • Need for Hypothesis Testing in Business
  • Two tailed, Left tailed & Right tailed test
  • Hypothesis Testing Outcomes : Type I & II erros
  • Parametric vs Non-Parametric Testing
  • Parametric Tests , T - Tests : One sample, two sample, Paired
  • One Way ANOVA
  • Importance of Parametric Tests
  • Non Parametric Tests : Chi-Square, Mann-Whitney, Kruskal-Wallis etc.,
  • Which Test to Choose?
  • Ascerting accuracy of Data

Module 6: Correlation & Regression

  • Introduction to Regression
  • Type of Regression
  • Hands on of Regression with R and Python.
  • Correlation
  • Weak and Strong Correlation
  • Finding Correlation with R and Python

The following topics are covered here

Module 1: Machine Learning Introduction

  • What is Machine Learning
  • Applications of Machine Learning
  • Machine Learning vs Artificial Intelligence
  • Machine Learning Languages and platforms
  • Machine Learning vs Statistical Modelling

Module 2: Machine Learning Algorithms

  • Popular Machine Learning Algorithms
  • Clustering, Classification and Regression
  • Supervised vs Unsupervised Learning
  • Application of Supervised Learning Algorithms
  • Application of Unsupervised Learning Algorithms
  • Overview of modeling Machine Learning Algorithm : Train , Evaluation and Testing.
  • How to choose Machine Learning Algorithm?

Module 3: Supervised Learning

  • Simple Linear Regression : Theory, Implementing in Python (and R), Working on use case.
  • Multiple Linear Regression : Theory, Implementing in Python (and R), Working on use case.
  • K-Nearest Neighbors : Theory, Implementing in Python (and R), KNN advantages, Working on use case.
  • Decision Trees : Theory, Implementing in Python (and R), Decision |Tree Pros and Cons, Working on use case.

Module 4: Unsupervised Learning

  • K-Means Clustering: Theory, Euclidean Distance method.
  • K-Means hands on with Python (and R)
  • K-Means Advantages & Disadvantages

The following topics are covered here

Module 1: Advanced Machine Learning Concepts

  • Tuning with Hyper parameters.
  • Popular ML algorithms,
  • Clustering, classification and regression,
  • Supervised vs unsupervised.
  • Choice of ML algorithm
  • Grid Search vs Random search cross validation

Module 2: Principle Component Analysis (PCA)

  • Key concepts of dimensionality reduction
  • PCA theory
  • Hands on coding.
  • case study on PCA

Module 3: Random Forest - Ensemble

  • Key concepts of Randon Forest
  • Hands on coding.
  • Pros and cons.
  • case study on Random Forest

Module 4: Support Vector Machine (SVM)

  • Key concepts of Support Vector Machine.
  • Hands on coding.
  • Pros and Cons.
  • case study on SVM

Module 5: Natural Language Processing (NLP)

  • Key concepts of NLP.
  • Hands on coding.
  • Pros and Cons.
  • Text Processing with Vectorization
  • Sentiment analysis with TextBlob
  • Twitter sentiment analysis

Module 6: Naïve Bayes Classifier

  • Key concepts of Naive Bayes.
  • Hands on coding.
  • Pros and Cons
  • Naïve Bayes for text classification
  • New articles tagging

Module 7: Artificial Neural Network (ANN)

  • Basic ANN network for Regression and Classification
  • Hands on coding.
  • Pros and Cons
  • Case study on ANN, MLP

Module 8: Tensorflow overview and Deep Learning Intro

  • Tensorflow work flow demo
  • Introduction to deep learning.

Module 1: Tableau Introduction

  • Tableau Interface
  • Dimensions and measures
  • Filter shelf
  • Distributing and publishing

Module 2: Connecting to Data Source

  • Connecting to sources, Excel, Data bases, Api , Pdf
  • Extracting and interpreting data.

Module 3: Visual Analytics

  • Charts and plots with Super Store data

Module 4: Forecasting

  • Forecasting time series data

Module 1: Understanding Business Case

  • Components of Business Case.
  • ROI calculation techniques.
  • Scoping

Module 2: Writing Data Science Business Case

  • Defining Business opportunity.
  • Translating to Data Science problem.
  • Creating project plan

Module 3: Benefits Analysis

  • Demonstrating break even and benefits analysis with Data Science Solutions.
  • IRR benefits analyis
  • Discounted Cash Flow

Module 4: Starting project, Setting up Team and closing

  • Initiating Project
  • Setting up the Team
  • Controling project delivery
  • Closing project.

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