1. Introduction to Python and Basics

a) Variables and why variables?

b) Data types – Primitive

c) Comments

d) Operators

1. Arithmetic Operator
2. Comparison Operator
3. Logical Operator
4. Assignment Operator
5. Membership Operator
6. Identity Operator
7. Bitwise Operator

e) Type Casting

f) Input Functions

g) Ways of Printing

h) ord() and Chr() -> ASCII Concept

2. Conditional Statements

a) Only IF Condition

b) IF – Else Condition

c) ELIF Ladder

d) Nested Conditional Statements

e) Multiple Conditional Statements

3. Loops

a) While Loop

b) For Loop

With Range

Without Range

c) Loop Control/ Transfer statement

break

continue

pass

4. Non – Primitive Data types

a) String and String Inbuilt Functions

b) List and List Inbuilt Functions

c) Tuples and Tuples Inbuilt Functions

d) Set and Set Inbuilt Functions

e) Dictionary and Dictionary Inbuilt Function

5. File Handling

a) Playing .csv file

b) Playing with .txt file

6. Handling Json Data

a) Reading Json Data

b) Traversing through Json Data

c) Saving Json Data

7. Functions

a) Function Creation and Usage

b) Types of Functions

c) Types of functions arguments

d) Nested Function

8. Exception Handling

a) Try, Except and Finally

b) Raise and Assert

9. Regular Expressions

a) Findall()

b) Split()

c) Sub()

10. Introduction to Classes and Objects

a) why Classes ?and Class and Object creation.

b) OOPS Concept

1. Inheritance and Types of Inheritance
2. Abstract Classes
3. Polymorphism and why Polymorphism?
4. Access modifier 

c) Public and Private

Static, Instance and Class Method

11. Data structure and algorithm

a) Data types

b) Stack and Queues

c) Linked List

d) Trees

e) Graphs

12. Searching & Sorting Algorithms

13. Special Concepts

a) Lambda Functions

b) Zip()

c) Map()

d) Reduce()

e) Filter()

f) Args( * ) and Kwargs( ** )

14. Decorators and Why Decorators?

15. Generators and Why Generators?

1. Introduction To Basic Statistics using Python

2. Data Types

3. Types of Statistics

4. Measure Of central tendency

5. Measures of Dispersion

6. Graphical Techniques

7. Skewness & Kurtosis

8. Box Plot

9. Sample vs Population

10. Types of Variables (continuous and discrete)

11. Normal Distribution,

12. Hypothesis Testing

13. Type 1 Error, Type 2

14. Error

15. P value, R2, z-test,

16. t-test, Anova Test etc..

1. Exploratory Data Analysis (EDA)-1 2. Linear egression

3. Logistic regression

4. Unsupervised Machine Learning 5. Supervised Machine Learning

6. Model validation techniques

UNSUPERVISED MACHINE LEARNING

1. Supervised vs Unsupervised learning

2. Hierarchical Clustering / Agglomerative Clustering

3. Types of Linkages

a) Single Linkage / Nearest Neighbor

b) Complete Linkage / Farthest Neighbor

c) Average Linkage

d) Centroid Linkage

4. Visualization of clustering algorithm using Dendrogram 5. K –Means

6. DBSCAN

7. Dimension Reduction Techniques

a) Why dimension reduction

b) PCA

c) t-SNE

8. Recommendation System

SUPERVISED MACHINE LEARNING

1. Decision Tree

2. Exploratory Data Analysis – 2 3. Feature Engineering

4. Model Validation Methods

5. Ensemble Techniques

a) XGBoost

b) Random forest

6. KNN (k-Nearest Neighbor)

8. Regularization Techniques

9. Text Mining

10. Natural Language Processing 11. Naive Bayes

12. Forecasting

LIBRARY USED IN ML

NumPy, Pandas, Seaborn ,Matplotlib, Scikit-learn

1. ANN – Artificial Neural Network

2. CNN – Convolutional Neural Network 3. RNN – Recurrent Neural Network

4. LSTM – Long Short-Term Memory

5. Stacked LSTM for forecasting

6. Object Detection by RCNN to YOLO

7. Image processing using open CV

LIBRARY USED IN DL

NumPy, Pandas, Seaborn ,Matplotlib, Scikit-learn

1. What is Data Visualization?

a) Why Visualization?

b) Importance of Visualizing Data

c) Principles of Visualizations

d) Goal of Data Visualization

2. Tableau – Data Visualization Tool

a) Pivot Tables

b) Split Tables

c) Hiding

d) Rename and Aliases

e) Data Interpretation

3. Tableau User Interface

a)Understanding about Data Types and Visual Cues

4. Basic Chart types

a) Text Tables, Highlight Tables, Heat Map b) Pie Chart, Tree Chart

c) Bar Charts, Circle Charts

5. Intermediate Chart

a) Time Series Charts

b) Time Series Hands-On

c) Dual Lines

d) Dual Combination

6. Advanced Charts

a) Bullet Chart

b) Scatter Plot

c) Correlation Analysis

d) Regression Analysis

e) Trend lines

f) Histogram

g) Bin Sizes in Tableau

h) Box Plot

i) Pareto Chart

j) Donut Chart, Word Cloud

7. Maps in Tableau

a) Types of Maps in Tableau

b) Polygon Maps

c) Custom Geo coding

8. Background Image

a) Get Background Image and highlight the data on it b) Creating Data Extracts

c) Filters

9. Data Connectivity in-depth understanding

a) Joins

b) Unions

c) Data Blending

d) Cross Database Joins

e) Sets

f) Groups

e) Parameters

10. Creating Calculated Fields

a) Logical Functions

b) Case-If Function

c) ZN Function

d) Else-If Function

e) Quick Table Calculations

f) Level of Detail (LoD)

11. Responsive Tool Tips

a) Dashboards

1. Data Base

2. DBMS Data Base Management System

3. Difference between SQL and MySQL

4. RDBMS

5. Data Types

6. MySQL Workbench

7. How to create table

8. SQL Command

a) DDL command (Data Definition Language)

b) DML command (Data Manipulation language)

c) DQL command (Data Query Language)

d) DCL command (Data control Language)

e) TCL command (Transmission Control Language)

9. Pattern Matching

10. Ascending and Descending order

11. Order by

12. Functions

a) Row level

b) Group / aggregate level

13. Joins

a) Right

b) Left

c) Cross

d) Self

14. Data Base Constraints / Restriction

15. Acid Properties

16. Data base Objects

a) Table

b) View

c) Index

d) Sequence

e) Stored procedure

f) Function

e) Triggers

17. Sub-query and windows