Data Science Training

Fundamentals of Data Science
and Machine Learning

Introduction to Data Science

Need of Data Science

BigData and Data Science’

Data Science and machine learning

Data Science Life Cycle

Data Science Platform

Data Science Use Cases

Skill Required for Data Science

Mathematics For Data Science

Linear Algebra

Vectors

Matrices

Optimization

Theory Of optimization

Gradients Descent

Introduction to Statistics

Descriptive vs. Inferential Statistics

Types of data

Measures of central tendency and dispersion

Hypothesis & inferences

Hypothesis Testing

Confidence Interval

Central Limit Theorem

Probability and Probability Distributions

Probability Theory

Conditional Probability

Data Distribution

Distribution Functions

Normal Distributio

Binomial Distribution 

2: RDBMS: SQL2: RDBMS: SQL

An Introduction to
RDBMS & SQL

Data Retrieval with
SQL

Pattern matching with
wildcards

Basics of sorting

Session summary

Order by clause

Aggregate functions

Group by clause

Having clause

Nested queries

Inner join

Multi join

Outer join

Adding and Deleting
columns

Changing column name
and Data Type

Creating Table from
existing Table

Changing Constraints
Foreign key

Introduction to Stored
Procedures.

3: Python for Data Science: Python for Data Science

An Introduction to Python

Why Python , its
Unique Feature and where to use it?

Python environment
Setup/shell

Installing Anaconda

Understanding the
Jupyter notebook

Python Identifiers,
Keywords

Discussion about
installed module s and packages

Conditional Statement ,Loops and File Handling

Python Data Types and
Variable

Condition and Loops in
Python

Decorators

Python Modules &
Packages

Python Files and
Directories manipulations

Use various files and
directory functions for OS operations

Python Core Objects and Functions

Built in modules
(Library Functions)

Numeric and Math’s
Module

String/List/Dictionaries/Tuple

Complex Data
structures in Python

Python built in
function

Python user defined
functions

4. Introduction to NumPy

Array Operations

Arrays Functions

Array Mathematics

Array Manipulation

Array I/O

Importing Files with
Numpy

5. Data Manipulation with Pandas

Data Frames

I/O

Selection in DFs

Retrieving in DFs

Applying Functions

Reshaping the DFs –
Pivot

Combining DFs

Merge

Join

Inverse, Transpose, Trace,   Norms , Rank etc

Eigen Values & vectors

SVDs

Data Alignment 

6. SciPy

Matrices Operations

Create matrices

Matrices Decomposition

7.Visualization with Seaborn 

Seaborn Installatio

8. Visualization with Matplotlib

Matplotlib
Installation

Matplotlib Basic Plots
& it’s Containers

Matplotlib components
and properties

Pylab & Pyplot

Scatter plots

2D Plots-

Histograms

Bar Graphs

Pie Charts

Box Plots

Customization

Store Plots

9. SciKit Learn

Basics

Data Loading

Train/Test Data
generation

Preprocessing

Generate Model

Evaluate Models

10. Descriptive Statistics

. Data understanding

Observations,
variables, and data matrices

Types of variables

Measures of Central
Tendency

Arithmetic Mean /
Average

Merits & Demerits
of Arithmetic Mean and Mode Merits
& Demerits of Mode and Median

Merits & Demerits
of Median Variance

11. Probability Basics

Notation and
Terminology

Unions and
Intersections

Conditional
Probability and Independence

12. Probability Distributions

Random Variable

Probability
Distributions

Probability Mass
Function

Parameters vs.
Statistics

Binomial Distribution

Poisson Distribution

Normal Distribution

Standard Normal
Distribution

Central Limit Theorem

Cumulative
Distribution function

13.  Tests of Hypothesis

Large Sample Test

Small Sample Test

One Sample: Testing
Population Mean

Hypothesis in One
Sample z-test

Two Sample: Testing
Population Mean

One Sample t-test –
Two Sample t-test

Paired t-test

Hypothesis in Paired
Samples t-test

Chi-Square test

14. Data Analysis

Case study- Netflix

Deep analysis on
Netflix data