DATA SCIENCE

Learn with HADWIK to gain hands-on practice using real data science tools & real-world data sets. Learn data science,R, Python & SQL,Tableau, analyze & visualize data, build machine learning models .

Data Science & Analytics

Special Features of Training Program

Kick-start your Data Science journey

Course Description

In the advent of availability of Data everywhere organizations expect that their employees are equipped with the skill sets of Data Analysis in the given business environment. Learning Analytics or Data Science is no more a luxury rather has become a necessity.

This Training Programme/Certification Programme is specially designed for students/working professionals from all streams. This programme will provide special skill sets to students/working professionals who can use the same right from day one in their job roles of any kind be in Human Resource, Marketing, Finance or Supply-Chain Management or as a Data Scientist in analytics companies.

What you will Learn !

DATA SCIENCE SYLLABUS

Structure of Topics

Introduction to Business Analytics

Case Study: How Ford Motor Company was able to retain their customers and increase sales? Business Analytics Landscape

R Programming

R Programming Basics, Data Frame & Structures, Import and Export of files

Data Manipulation

Data Manipulation, Slicing & Dicing Data for deeper insights

Statistical Concepts

Sample Statistics, Central Tendency & Dispersion, Standard Error of Mean

Data Visualization

Bar Plot, Pie Chart, Histograms, Box Plots, Pair Plots, Heat Maps

Hypothesis Testing

Hypothesis Testing, Type I & II Errors, T Tests

Analysis of Variance

Analysis of Variance, One Way ANOVA and Two-Way ANOVA

Correlation & Regression

Dependency, Best Fit Line, Slope between driver and driven variable

Multiple Regression

Multicollinearity, Auto Correlation, Variance Inflation Factor

Decision Trees for Classification

Tree Building Mechanism, Information Gain, Gini Index & Entropy

Decision Trees for Regression

Tree Pruning, RMSE, MSE Criterion

Bagging and Random Forests

Ensemble Models, Out of Bag efficiency, Feature Selection

Adaptive Boosting

More attention towards weak learners, Weight Adjustment, Boot Strapping

Logistic Regression

Odds versus Probability, Logit Function, Threshold Level

Support Vector Machines

Hyper Plane, Maximum Margin, Support Vectors, Kernel Trick

Nearest Neighbour Classifiers

Euclidean Distance, Choosing Optimum numbers of Neighbours

Structure of Topics

Cluster Analysis

Unsupervised Classification Method, Single, Complete, Average & Ward Linkages

K-Means Clustering

Within Column Sum Squared Criterion, Optimum number of Clusters, Cluster Profiling

Neural Networks

Analogy with Human Brain, Activation Functions, Network Architecture

Text Mining

Importing Text Data, Data Cleaning, Word-Cloud, Sentiment Analysis

Decision Tree Analysis

Decision or Act Nodes, Event or Chance Nodes, Payoffs, Picturization of Tree

Python for Analytics

Key Python Libraries, Numpy, Pandas, Seaborn, Matplotlib, Data Visualization

Python for Analytics

Data Manipulation, Slicing & Dicing Data for deeper insights with Python

Linear Regression-Python

Regression Coefficients and testing of slopes

Logistic Regression-Python

Logit Function, Classification Matrix

Deep Learning -I

Tensor Flows and Keras, Deep Nets

Object Recognition

Image Processing and Recognition

SaS -Introduction

Reading, Writing and Importing Data

SaS- Data Manipulation

Slicing and Dicing of Data through PROC Commands

SQL Basics – I

Query Language Fundamentals

SQL -Applications

Table Formulation & Reporting

Tableau

Data Visualization

Capstone Project

Final Project

Requirements :

Bachelor’s Degree with minimum 50% or equivalent passing marks. No coding experience required.
Engineers, Marketing & Sales Professionals, Freshers, Domain Experts, Software & IT Professionals

Job titles in Data Science Career