Course details
COURSE MAJORS:
Introduction to Big Data | Python for Data Science | SQL Primer for
Data Science | Data Visualization with Python | Statistics for Data
Science | Exploratory Data Analysis | Linear Algebra for Data Science
| Research Methodology | Supervised Machine Learning-Regression
Analysis | Supervised Machine Learning-Classification | Unsupervised
Machine Learning - Clustering Analysis & Association Rules | Hyper
Parameter Optimization | Model Performance Assessment | Machine
Learning- Natural Language Processing | Artificial Neural Networks
with TensorFlow 2 | Convolutional Neural Networks | Real-world
Project Implementations (15+)
INTRODUCTION TO BIG DATA
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Introduction to Big Data, State of the practice in analytics
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Current Analytical Architecture, Drivers of Big Data, Emerging Big Data Ecosystem
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Big Data Analytics Project Life Cycle: Overview, Phase 1- Discovery, Phase 2- Data
preparation, Phase 3-Model Planning, Phase 4- Model Building, Phase 5- Communicate Results,
Phase 6- Operationalize.
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Introduction to Machine Learning
PYTHON FOR DATA SCIENCE
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Module 1: Introduction to Python, What is Python and history of Python?, Unique features
of Python, Python-2 and Python-3 differences, Install Python and Environment Setup, First
Python Program, Python Identifiers, Keywords and Indentation, Comments and document
interlude in Python, Command line arguments, Getting User Input, Python Data Types, What
are variables, Python Core objects and Functions, Number and Maths.
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Module 2: List, Ranges & Tuples in Python, Introduction, Lists in Python, Understanding
Iterators, Generators, Comprehensions and Lambda Expressions, Introduction, and Yield,
Next and Ranges, Ordered Sets with tuples.
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Module 3: Python Dictionaries and Sets, Dictionaries, More on Dictionaries, Sets, Python
Sets Example.
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Module 4: Input and Output in Python, Reading and writing in the file, Writing Binary Files
Manually, Text Files, Appending to Files and Using Pickle to Write Binary Files.
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Module 5: Python built in function, Python user defined functions, Python packages functions,
Defining and calling Function, The anonymous Functions Loops and statement in Python,
Python Modules & Packages.
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Module 6: Python Regular Expressions: What are regular expressions? The match Function,
the search Function, Matching vs searching, Search and Replace, Extended Regular
Expressions, Wildcard.
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Module 7: Python For Data Analysis Numpy : Introduction to Numpy, Creating arrays,
Using arrays and Scalars, Indexing Arrays, Array Transposition, Universal Array Function,
Array Processing, Array Input and Output.
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Module 8: Python for Data Analysis Pandas: What is pandas? Where it is used? Series
in pandas, Index objects Reindex, Drop Entry, Selecting Entries, Data Alignment, Rank and Sort, Summary Statistics, Missing Data, index Hierarchy, Matplotlib: Python for Data
Visualization.
Module 9: Using Databases in Python, Python MySQL Database Access, Install the MySQLDB
and other Packages, Create Database Connection, CREATE, INSERT, READ, UPDATE and
DELETE Operation, DML and DDL Operation with Databases, Handling Database Errors,
Web Scraping in Python.
SQL PRIMER FOR DATA SCIENCE
Introduction
Data vs. Information
History of the Database
Major Transformations in Computing
Entities and Attributes
Conceptual and Physical Models
Entities, Instance, Attributes, and Identifiers
Entity Relationship Modeling and ERDs
SELECT and WHERE
Columns, Characters, and Rows
Limit Rows Selected | Comparison Operators
WHERE, ORDER BY, GROUP BY, HAVING and Intro to Functions
Logical Comparisons and Precedence Rules
Sorting Rows
Introduction to Functions
Single Row Functions
Character Functions | Number Functions
Date Functions | Conversions Functions
General Functions
Data Manipulation Language (DML)
INSERT Statements
Updating Column Values and Deleting Rows
Data Definition Language (DDL)
Creating Tables | Using Data Types • Modifying a Table
Constraints
Intro to Constraints; NOT and UNIQUE
PRIMARY KEY, FOREIGN KEY, and CHECK
STATISTICS FOR DATA SCIENCE
Data types and its measures
Random Variables and its applications
Introduction to Probability with examples
Sampling Techniques - Why and How
Measures of Central Tendency- Mean, Median, Mode
Measures of Dispersion- Variance, Standard Deviation, Range
Measures of Skewness & Kurtosis
•Normality tests for dataset
•Basic Graph Representations- • Bar Chart, Histogram, Box Plot, Scatterplot
•Probability Distributions
Continuous Probability Distribution
Normal Distribution
Standard Normal Distribution(Z)
F-Distribution
Chi-Square Distribution
Discrete Probability Distribution
Binomial Distribution
Poisson Distribution
•Building Normal Q-Q Plot and its Interpretation
•Central Limit Theorem for sampling variations
Confidence Interval - Computation and analysis
EXPLORATORY DATA ANALYISIS
•Data Cleansing (Dealing with Missing Data, Outlier Detection)
•Feature Engineering (Label Encoding, One-Hot Encoding)
•Sampling Techniques (Balanced, Stratified, ...)
•Data Partitioning (Create Training + Validation + Test Data Set)
•Transformations (Normalization, Standardization, Scaling, Pivoting)
•Data Replacement (Cutting, Splitting, Merging, ...)
•Imputation (Replacement of Missing Observations with Statistical Algorithms)
LINEAR ALGEBRA FOR DATA SCIENCE
•Motivation - Why to learn Linear Algebra?
•Representation of problems in Linear Algebra
•Visualizing the problem: Line
•System of linear equations
•Planes
•Matrix
•Terms related to Matrix
•Basic operations on Matrix
Updated on 06 September, 2022 - Read Less