1. Introduction(1 Session)
Course structure, brief intro about Python, Advantages, Applications and Opportunities
2. Installation and Execution(1 Session)
Setup and Execution, Interactive Prompts, OS terminals, IDEs, IDLE and explanation of Jupyter Notebooks
3. Variables and Datatypes(1 Session)
Naming Variables, Functions, Classes etc.,
4. Numbers and Operator(1 Session)
Integers, Floating point numbers, Complex, Binary, Octal, Hexadecimal, Long
5. Strings and String functions - Part 1(1 Session)
Represent Characters, Words or Sentences, Single, Double or Triple Quotes
6. Strings and String functions - Part 2(1 Session)
Indexing
7. Strings and String functions - Part 3(1 Session)
String Functions
8. Lists(1 Session)
List Indexing, List Slicing, Task, List Append, List Clear, List Extend
9. Dictionaries(1 Session)
Key-Value Pairs, Syntax, Unordered
10. Sets and Tuples(1 Session)
Unordered Collection, Can contain numbers, strings or tuples, Represented using, Repetitions not allowed, Operations resemble set theory in mathematics
11. Statements & Conditionals(1 Session)
Assignments, Function statements, Control statements, Namespaces, Generators, Exceptions, Debuggers, Context managers, Import Statements, Object declarations
12. Loops(1 Session)
Conditional Statement & Loop Statement
13. Statements & Conditionals Part 2(1 Session)
Continuation of Statements & Conditionals Part 2
14. Functions - Part 1(1 Session)
Functions Intro & Arguments
15. Functions - Part 2(1 Session)
Variable Scopes
16. Exception Handling and Modules(1 Session)
Exception Handling and Modules
17. Classes(1 Session)
Introduction to OOP, Classes, Objects
18. Application 1(1 Session)
Project Intro, Text Files, Quiz & Quiz Solutions and JSON and XML Files
19. Application 2(1 Session)
Reading CSV Data and Writing to Excel File
20. Application 3(1 Session)
HTTP & API Requests
21. MongoDB Introduction and Installation(1 Session)
MongoDB Installation in Windows and Linux
22. MongoDB Crud Operations(1 Session)
Crud Operations
23. Data Modeling(1 Session)
Data Modeling and Index
24. Administration(1 Session)
Administration
25. Tools(1 Session)
MongoDB Tools
26. Security(1 Session)
MongoDB Security
27. Aggregation(1 Session)
MongoDB Aggregation
28. Replication(1 Session)
MongoDB Replication
29. Sharding(1 Session)
MongoDB Sharding
30. OPS Manager(1 Session)
Ops Manager
31. Introduction to Machine Learning (1 Session)
All the Basics, Data Science Modules and Application of Machine Learning and its tools
32. Introduction to Python and IDE(1 Session)
Different flavors and platforms, Install Anaconda, Python IDE: Spyder, Setting your work directory, The libraries that you should know, IPython and Python Notebook
33. Introduction to Python Programming and Data types(1 Session)
Concept of Program and Programming, Programming constructs in Python. Values, Variables, Strings, Tuples, Lists & Dictonaries, Accessing Elements. Cloning Slices, Aliasing Vs Cloning and Iterations
34. Numpy Packages and its Handson(1 Session)
Numpy Package, Creating ndarrays, Indexing, Data Processing using Arrays and File Input & Output.
35. Pandas Packages and its Handson(1 Session)
Introduction to Pandas Packages, Pandas Packages Handson, Create the Dataframe and List and Dataframe and Set and OTS Handson
36. Data Visualization using Matplotlib Packages(1 Session)
Introduction to Data Visualization, Handson Line Graph, Pie Chart, Bar Graph and Scatter Plot, Handson Exercise using Matlotlib
37. Descriptive Statistics using Pandas (1 Session)
Introduction to Descriptive Statistics and Central Tendency, Measure of Dispersion, Distribution of Shape and Outliers, Descriptive Statistics Handson
38. Hypothesis Testing and its Process (1 Session)
Introduction to Hypothesis, Hypothesis Formulation and its process and Errors in Hypothesis
39. Inferential Statistics using Scipy(1 Session)
Introduction to Inferential Statistics, Non Parametric Test using Scipy, Non Parametric Test Handson, Parametric Test Handson using Scipy
40. Data Preparation Process and EDA(1 Session)
Data Preparation Process and Exploratory Data Analysis (EDA)
41. Measurement and Scaling(1 Session)
Primary Scales - Data Analysis, Comparitive Scaling Techniques, and Non Comparitive Scaling Techniques
42. Data Collection and Data Treatment(1 Session)
Data Collection Methods & Data Types and Data Source error & Data Treatment
43. Correlation and Application(1 Session)
Introduction to Correlation, Product Movement Correlation and its application, Correlation using Hypothesis Test, Correlation Handson using Scipy Packages
44. Questionnaire Design Process (1 Session)
Questionnaire Design Process and Application on Questionnaire Design
45. Linear Regression and its Application (1 Session)
Linear Regression Introduction, Simple Regression using Hypothesis Testing, Multiple Regression and its assumption, and Simple and Multiple Regression Handson using Python codes
46. Probability and its Bayes Theorem (1 Session)
Probability Introduction and its Application and Bayes Theorem using Probability
47. Discrete Probability Distribution(1 Session)
Discrete Probability Distribution - Binomial and Possion and Hypergeometric Distribution and its Application
48. Continuous Probability Distribution (1 Session)
Continuous Probability - Uniform, Normal and Exponential Distribution and Application of Continuous Probability Distribution
49. ANOVA - Analysis of Variance(1 Session)
ANOVA Using Hypothesis Testing and ANOVA - Handson using Python
50. ANCOVA - Analysis of Covariance(1 Session)
ANCOVA using Hypothesis Testing and Handson
51. Discrimiant Analysis(1 Session)
Discriminant Analysis and its Application and Discriminant Analysis - Handson using Python
52. Logistic Regression(1 Session)
Logistic Regression - Methods and Application and Logistic Regression - Handson using Sklearn Packages
53. Time Series(1 Session)
Timeseries - Components and Smothening and Timeseries - Forecasting Methods and Handson
54. Factor Analysis(1 Session)
Factor Analysis - PCA and Rotation Method, Factor Analysis - Real Time Case Study and Factor Analysis - Hands on Using Sklearn
55. Cluster Analysis(1 Session)
Hierarchical Clustering, Non Hierarchical Clustering and Handson Using Cluster Analysis
56. Data Architecture(1 Session)
Data Architecture and Data Warehousing and Multi Dimentional Model
57. Association Rule - Apriori Algorithm(1 Session)
Apriori Algorithm, Market Basket Analysis and Handson using Apriori Algorithm
58. Artificial Neural Network(1 Session)
ANN - Single and Multi Layered Architecture and ANN - With Real time Example
59. Decision Tree(1 Session)
Decision Tree - Introduction and Implementation Logics and Decision Tree - Handson using Sklearn Packages
60. Random Forest (1 Session)
Random Forest - Introduction and Implementation Logics and Random Forest - Handson using Sklearn Packages
61. Naive Bayes Classification(1 Session)
Naive Bayes - Introduction and Implementation Logics and Naive Bayes - Handson using Sklearn Packages
62. K Neariest Neighbour (1 Session)
KNN - Introduction and Implementation Logics and KNN - Handson using Sklearn Packages
63. Support Vector Machine (1 Session)
SVM - Introduction and Implementation Logics and SVM - Handson using Sklearn Packages
64. Artificial Neural Network (ANN)(1 Session)
Artificial Neural Network Introduction, ANN - Architecture and Schematic Diagram, ANN – Architectural Types, Pre-processing steps of ANN etc.,
65. K - Nearest Neighbour (KNN) (1 Session)
K - Nearest Neighbour Introduction, K - Nearest Neighbour Algorithm, Pre-Processing your dataset for KNN, How to measure "Neraby" etc.,
66. Time Series (1 Session)
Time Series Basics, Time Series Component, Smoothing Methods, Trend Based Forecasting, Time Series using R Programming, Auto Regressive (AR) Model etc.,
67. Marketing Analytics and its Case Study(1 Session)
Introduction to Retail and Marketing Analytics, Customer Analytics, Churn Modelling using Operational Analytics, Market Basket Analysis using Marketing Analytics, Price and In store Promotion using Retail Analytics.
68. Operational Analytics and its Case Study (1 Session)
Scope of the Project and Economic Industry Analysis, Company Analysis and its Competitor Analysis, Project Specific Analysis and Theoretical Framework, Limitations, Findings and Recommendation.
69. Finance Analytics and its Application (1 Session)
Credit Risk Analytics using Logistic Regression, Merger and Acquisitions Analysis using Financial Analytics, Financial Ratio Analysis using Analytics, Companys Financial Analytics
70. Project Session - How to use the inbuilt AI based Code Editor(1 Session)
Learn why we have this super cool AI based code editor for you. Also, learn how to make the best use of it.
71. Project Session - Web scraping and Automation using Python - Part 1(1 Session)
This part covers BeautifulSoup module HTML Parsing of web pages,Crawling links Storing Data in files.
72. Project Session - Web scraping and Automation using Python - Part 2(1 Session)
This part covers Automatic Scraping using Selenium Handling, API requests Bottle necks in Scraping.
73. Project Session - Data mining and Visualization from Google sheets - Part 1(1 Session)
This part covers Numerical computation in python using Numpy, Pandas, Scipy. Visualisation using Matplotlib, Accessing Google sheets data using Google sheets API
74. Project Session - Data mining and Visualization from Google sheets - Part 2(1 Session)
This part covers Project Intro, Text Files, Quiz & Quiz Solutions and JSON and XML Files, Reading CSV Data and Writing to Excel File, HTTP & API Requests
75. Project Session - API Integration Using Python(1 Session)
This part covers advanced topics in Python. You will be integrating two third party APIs and create your own project that you can show off on Github.
76. Project Session - Realtime project suggestions from industry experts(1 Session)
Get introduced to few Project ideas that you can work on by yourself.