Curriculum
31 Sections
58 Lessons
24 Weeks
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Module 1: PYTHON: INTRODUCTION TO DATA SCIENCE WITH PYTHON
16
1.0
MODULE 1: PYTHON: INTRODUCTION TO DATA SCIENCE WITH PYTHON
1.1
What is analytics & Data Science?
1.2
Common Terms in Analytics
1.3
Analytics vs. Data warehousing, OLAP, MIS Reporting
1.4
Relevance in industry and need of real time world
1.5
Types of problems and business objectives in various industries
1.6
How leading companies are harnessing the power of analytics?
1.7
How leading companies are harnessing the power of analytics?
1.8
Critical success drivers
1.9
Overview of analytics tools & their popularity
1.10
Analytics Methodology & problem solving framework
1.11
List of steps in Analytics projects
1.12
Identify the most appropriate solution design for the given problem statement
1.13
Project plan for Analytics project & key milestones based on effort estimates
1.14
Build Resource plan for analytics project
1.15
Why Python for data science?
Module 2: Employee Central Core
22
2.0
MODULE 2: PYTHON FOUNDATION: ESSENTIALS (CORE)
2.1
Overview of Python- Starting with Python
2.2
Why Python for data science?
2.3
Anaconda vs. python
2.4
Introduction to installation of Python
2.5
Introduction to Python Editors & IDE’s(Jupyter,/Ipython)
2.6
Understand Jupyter notebook & Customize Settings
2.7
Concept of Packages – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc.)
2.8
Installing & loading Packages & Name Spaces
2.9
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
2.10
List and Dictionary Comprehensions
2.11
Variable & Value Labels – Date & Time Values
2.12
Basic Operations – Mathematical – string – date
2.13
Control flow & conditional statements
2.14
Debugging & Code profiling
2.15
Python Built-in Functions (Text, numeric, date, utility functions)
2.16
User defined functions – Lambda functions
2.17
Concept of apply functions
2.18
Python – Objects – OOPs concepts
2.19
How to create class and modules?
2.20
How to call classes and modules?
2.21
Concept of pipelines in Python
Module 3: PYTHON FOUNDATION: OPERATIONS WITH NUMPY (NUMERICAL PYTHON)
14
3.0
MODULE 3: PYTHON FOUNDATION: OPERATIONS WITH NUMPY (NUMERICAL PYTHON)
3.1
What is NumPy?
3.2
Overview of functions & methods in NumPy
3.3
Data structures in NumPy
3.4
Creating arrays and initializing
3.5
Reading arrays from files
3.6
Special initializing functions
3.7
Slicing and indexing
3.8
NumPy Maths
3.9
Combining arrays
3.10
Basic algebraic operations using NumPy arrays
3.11
Solving linear equations
3.12
Matrix inversions
3.13
Calculating Eigen vectors
Module 4: PYTHON FOUNDATION: OVERVIEW OF PANDAS
0
Module 5: PYTHON FOUNDATION: CLEANSING DATA WITH PYTHON
1
5.0
MODULE 5: PYTHON FOUNDATION: CLEANSING DATA WITH PYTHON
Module 6 : PYTHON FOUNDATION: VISUALIZATION USING PYTHON
1
6.0
MODULE 6: PYTHON FOUNDATION: VISUALIZATION USING PYTHON
Module 7: PYTHON FOUNDATION: BASIC STATISTICS & IMPLEMENTATION OF STATS METHODS IN PYTHON
1
7.0
MODULE 7: PYTHON FOUNDATION: BASIC STATISTICS & IMPLEMENTATION OF STATS METHODS IN PYTHON
Module 8: PYTHON MACHINE LEARNING: INTRODUCTION TO MACHINE LEARNING
1
8.0
MODULE 8: PYTHON MACHINE LEARNING: INTRODUCTION TO MACHINE LEARNING
Module 9: PYTHON MACHINE LEARNING: LEARNING ALGORITHMS
1
9.0
MODULE 9: PYTHON MACHINE LEARNING: LEARNING ALGORITHMS
Module 10: PYTHON MACHINE LEARNING: SUPERVISED LEARNING - REGRESSION PROBLEMS USING LINEAR REGRESSION
1
10.0
MODULE 10: PYTHON MACHINE LEARNING: SUPERVISED LEARNING – REGRESSION PROBLEMS USING LINEAR REGRESSION
MODULE 11 : PYTHON MACHINE LEARNING: SUPERVISED LEARNING: CLASSIFICATION PROBLEMS USING LOGISTIC REGRESSION
0
MODULE 12 : PYTHON MACHINE LEARNING: SUPERVISED LEARNING: CLASSIFICATION & REGRESSION PROBLEMS USING DECISION TREES
0
MODULE 13: PYTHON MACHINE LEARNING: SUPERVISED LEARNING: CLASSIFICATION & REGRESSION PROBLEMS USING ENSEMBLE LEARNING
0
MODULE 14 : PYTHON MACHINE LEARNING: SUPERVISED LEARNING: CLASSIFICATION & REGRESSION PROBLEMS USING KNN
0
MODULE 15 : PYTHON MACHINE LEARNING: SUPERVISED LEARNING: CLASSIFICATION & REGRESSION PROBLEMS USING BAYESIAN TECHNIQUES
0
MODULE 16: PYTHON MACHINE LEARNING: SUPERVISED LEARNING: REGRESSION & CLASSIFICATION PROBLEMS USING SUPPORT VECTOR MACHINES
0
MODULE 17 : PYTHON MACHINE LEARNING: UNSUPERVISED LEARNING: SEGMENTATION PROBLEMS USING CLUSTER ANALYSIS
0
MODULE 18 : PYTHON MACHINE LEARNING: UNSUPERVISED LEARNING: SEGMENTATION PROBLEMS USING CLUSTER ANALYSIS
0
MODULE 19 : PYTHON MACHINE LEARNING: FORECASTING OVERVIEW AND BASICS OF TIME SERIES
0
MODULE 20 : PYTHON MACHINE LEARNING: SUPERVISED LEARNING: FORECASTING PROBLEMS USING TIME SERIES ANALYSIS
0
MODULE 21 : PYTHON MACHINE LEARNING: EVALUATION OF FORECASTING
0
MODULE 22 : PYTHON TEXT MINING NLP/NLG: INTRODUCTION TO TEXT MINING
0
MODULE 23 : PYTHON TEXT MINING NLP/NLG: TEXT PROCESSING USING BASE PYTHON & PANDAS, REGULAR EXPRESSIONS
0
MODULE 24 : PYTHON TEXT MINING NLP/NLG: TEXT PROCESSING WITH SPECIALIZED MODULES LIKE NLTK, SKLEARN ETC
0
MODULE 25 : PYTHON TEXT MINING NLP/NLG: INITIAL DATA PROCESSING AND SIMPLE STATISTICAL TOOLS
0
MODULE 26 : PYTHON TEXT MINING NLP/NLG: ADVANCED DATA PROCESSING AND VISUALIZATION
0
MODULE 27 : PYTHON TEXT MINING NLP/NLG: ADVANCED DATA PROCESSING AND VISUALISATION
0
MODULE 28 : PYTHON TEXT MINING NLP/NLG: FINAL PROJECTS
0
MODULE 29 : TABLEAU: GETTING STARTED
0
MODULE 30 : TABLEAU: DATA HANDLING & SUMMARIES
0
MODULE 31 : TABLEAU: BUILDING ADVANCED REPORTS/ MAPS
0
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