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SCOPE OF DATA SCIENCE

 

Data Science is one of the fastest evolving fields & a Data Scientist’s job is one of the fastest growing and highest paid in tech.
As there is a cut-throat competition in the market, top organizations are turning their minds to data analytics to identify new market opportunities to design their services and products. Surveys show that 75% of top organizations consider data analytics an essential component of business performance. This is where data scientists come in. Data scientists know how to use their skills in math, statistics, programming, and other related subjects to organize large data sets. Then, they apply their knowledge to uncover solutions hidden in the data to take on business challenges and goals. They are thus able to contribute to their organization’s business goals. So, learning data science through effective training can give you a bright future.

“Data Scientist” has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! It is a rewarding career that allows you to solve some of the world’s most interesting problems!

As per Payscale.com, a Data Scientist (IT) with Big Data Analytics skills earns an average salary of Rs 706,750 per year in India.

 

COURSE OBJECTIVE

 

Xilytica is delighted to offer classroom program for candidates to help them build a career in the blooming field of Data Science.The program focuses on building foundation skills in business analytics and data science with in depth training of statistical platform & tools.

We will develop candidates to think analytically and solve business problems using data.
Our innovative approach to analytics training by combining deep knowledge and collaborative learning environment will help candidates to develop real skills in analytics.

Candidates taking this course can expect to gain the knowledge of analytical techniques required by organizations to take strategic decisions, and by solving case studies they will know how analytical techniques can be used to get real insights from real data. Candidates will be able to forecast sales, Identify customer segments, identify drivers of sales/profit, perform regression, analyze customer comments and do much more after the course.

In-depth course coverage, hands-on experience of entire data analytics project cycle, and case studies on real world analytics problems, cutting across different domains are the high points that make Skill Venue’s Data Science in R certification course a leap towards a successful analytics career.

 

WHO SHOULD TAKE THIS COURSE

 

  1. IT professionals looking for career or technology change to  Data Science & Analytics.
  2. BPO industry  or non technical  professionals who are looking for career in Data Science & Analytics.
  3. Engineering graduates who want to build a career in Data Science & Analytics.
  4. Non- technical & MBA graduates who are interested in building a career in Data Science.
  5. Anyone who likes manipulating data & loves getting insights out of data.
  6. Prerequisite – There are not prerequisite for this course except hard work & dedication.

 

 

Course Curriculum

Introduction
1.1 – Introduction to Course 00:00:00
Course Curriculum 00:00:00
What is Data Science 00:00:00
Introduction to Programming Language
What is Programming Language 00:00:00
Ecosystem of any programming language 00:00:00
DATA SCIENCE WITH R
Windows Installation Set-Up
How to Install R & R Studio on Windows Operating System 00:00:00
Linux Installation Set-Up
How to Install R & R Studio on Linux Operating System 00:00:00
Mac OS Installation Set-Up
How to Install R & R Studio on Mac OS Operating System 00:00:00
Introduction to Basic R
Introduction to R Basics 00:00:00
Arithmatic Operations in R 00:00:00
Variables 00:00:00
R Basic Data Types 00:00:00
Vector Basics 00:00:00
Vector Operations 00:00:00
Vector Indexing & Selecting 00:00:00
Getting Help in R & R Studio 00:00:00
Comparision Operators 00:00:00
R Quiz 00:00:00
R Matrices
Introduction to R matrices 00:00:00
Creating a Matrix 00:00:00
Arithmatic Operations in Matrix 00:00:00
Matrix Operations 00:00:00
Matrix Indexing & Selecting 00:00:00
Factor & Categorical Matrices 00:00:00
Quiz 2 00:00:00
R Data Frames
Introduction to R Data Frames 00:00:00
Data Frame Basics 00:00:00
Data Frame Indexing & Selecting 00:00:00
Data Frame Operations 00:00:00
List in R
Introduction to R Lists 00:00:00
Data Input & Output with R
Introduction to Data Input & Output with R 00:00:00
Reading Data from CSV Files 00:00:00
Writing Data to CSV Files 00:00:00
Reading Data from Excel File 00:00:00
Writing Data to Excel File 00:00:00
SQL with R 00:00:00
Web Scraping with R 00:00:00
R Programming basics
Introduction to Programming Basics 00:00:00
Logical Operators 00:00:00
If, Else, Else If Statements 00:00:00
While Loop 00:00:00
For Loop 00:00:00
Functions 00:00:00
R programming Advance
Introduction to Advance R Programming 00:00:00
Built in R functions 00:00:00
Apply Function 00:00:00
Math Functions with R 00:00:00
Regular expressions 00:00:00
Dates & Times Stamps 00:00:00
Data Manipulation with R
Data Manipulation with R Overview 00:00:00
Guide to Using Dplyr 00:00:00
Pipe Operator 00:00:00
Guide to Using Tidyr 00:00:00
Data Visualization with R
Overview of ggplot2 00:00:00
Histograms 00:00:00
Scatterplots 00:00:00
Barplots 00:00:00
Boxplots 00:00:00
2 Variable Plotting 00:00:00
coordinates & faceting 00:00:00
Themes 00:00:00
Data Visualization with R Project
Data Visualization with R Project 00:00:00
Introduction to Machine Learning with R
Introduction to Machine Learning with R 00:00:00
Machine Learning with R - Linear Regression
Introduction to Linear Regression 00:00:00
Linear Regression with R 00:00:00
Project on Linear Regressions
Project on Linear Regressions 00:00:00
Machine Learning with R - Logistic Regression
Introduction to Logistic Regression 00:00:00
Logistic Regression with R 00:00:00
Project on Logistic Regressions
Project on Logistic Regressions 00:00:00
Machine Learning with R - K nearest neighbor
Introduction to K nearest neighbor 00:00:00
K nearest Neighbor with R
K nearest Neighbor with R 00:00:00
Project on K nearest Neighbor
Project on K nearest Neighbor 00:00:00
Machine Learning with R - Decision Tree & Random Forests
Intorduction to tree methods 00:00:00
Decision Tree & Random Forests with R 00:00:00
Project on Decision Tree & Random Forests
Project on Decision Tree & Random Forests 00:00:00
Machine Learning with R - Support Vector Machines
Intorduction to Support Vector Machines 00:00:00
Support Vector Machines with R 00:00:00
Project on Support Vector Machines
Project on Support Vector Machines 00:00:00
Machine Learning with R - K - Means Clustering
Intorduction to K – Means Clustering 00:00:00
K – Means Clustering with R 00:00:00
Project on Support Vector Machines
Project on K – Means Clustering 00:00:00
Machine Learning with R - Natural Language Processing
Intorduction to Natural Language Processing 00:00:00
Natural Language Processing with R 00:00:00
Project on Natural Language Processing
Project on Natural Language Processing 00:00:00
Machine Learning with R - Neural Nets
Intorduction to Neural Nets 00:00:00
Neural Nets with R 00:00:00
Project on Neural Nets
Project on Neural Nets 00:00:00
DATA VISUALISATION WITH TABLEAU
Introduction to Tableau
Introduction to Tableau 00:00:00
Introduction
Course Curriculum 00:00:00
Installing Tableau
Installing Tableau for Windows 00:00:00
Installing Tableau for Mac OS 00:00:00
Tableau Basics: Your First Bar chart
The Business Challenge – Who Gets the Annual Bonus? 00:00:00
Connecting Tableau to a Data File – CSV File 00:00:00
Navigating Tableau 00:00:00
Creating Calculated Fields 00:00:00
Adding Colors 00:00:00
” Adding Labels and Formatting” 00:00:00
Exporting Your Worksheet 00:00:00
” Get The Viz” 00:00:00
Time series, Aggregation, and Filters
Section Intro 00:00:00
Working with Data Extracts in Tableau 00:00:00
Working with Time Series 00:00:00
Understanding Aggregation, Granularity, and Level of Detail 00:00:00
Creating an Area Chart & Learning About Highlighting 00:00:00
Adding a Filter and Quick Filter 00:00:00
Maps, Scatterplots, and Your First Dashboard
Section Intro 00:00:00
Joining Data in Tableau 00:00:00
“Creating a Map, Working with Hierarchies “ 00:00:00
Creating a Scatter Plot, Applying Filters to Multiple Worksheets 00:00:00
Let’s Create our First Dashboard! 00:00:00
Adding an Interactive Action – Filter 00:00:00
“Adding an Interactive Action – Highlighting “ 00:00:00
Quiz 00:00:00
Joining and Blending Data, PLUS: Dual Axis Charts
Section Intro 00:00:00
Understanding how LEFT, RIGHT, INNER, and OUTER Joins Work 00:00:00
Joins With Duplicate Values 00:00:00
Joining on Multiple Fields 00:00:00
The Showdown: Joining Data v.s. Blending Data in Tableau 00:00:00
Data Blending in Tableau 00:00:00
Dual Axis Chart 00:00:00
Creating Calculated Fields in a Blend (Advanced Topic) 00:00:00
Section Recap 00:00:00
Quiz 00:00:00
Table Calculations, Advanced Dashboards, Storytelling
Section Intro 00:00:00
Downloading the Dataset and Connecting to Tableau 00:00:00
Mapping: how to Set Geographical Roles 00:00:00
Creating Table Calculations for Gender 00:00:00
Creating Bins and Distributions For Age 00:00:00
Leveraging the Power of Parameters 00:00:00
How to Create a Tree Map Chart 00:00:00
Creating a Customer Segmentation Dashboard 00:00:00
Advanced Dashboard Interactivity 00:00:00
Analyzing the Customer Segmentation Dashboard * 00:00:00
Creating a Storyline 00:00:00
Quiz 00:00:00
Advanced Data Preparation
Section Intro 00:00:00
What Format Your Data Should Be In 00:00:00
Data Interpreter 00:00:00
Pivot 00:00:00
“Splitting a Column into Multiple Columns “ 00:00:00
MetaData Grid 00:00:00
Fixing Geographical Data Errors in Tableau 00:00:00
Quiz 00:00:00
What's new in Tableau 10
Section Intro 00:00:00
“The Challenge: Startup Expansion Analytics “ 00:00:00
Custom Territories Via Groups 00:00:00
Custom Territories Via Geographic Roles 00:00:00
Adding a Highlighter 00:00:00
Clustering In Tableau 00:00:00
Cross-Database Joins 00:00:00
Modeling With Clusters 00:00:00
Saving Your Clusters 00:00:00
New Design Features 00:00:00
New Mobile Features 00:00:00
Section Recap 00:00:00
Quiz 00:00:00
Advance
Groups and Sets
Section Intro 00:00:00
Project Brief: 1,000 Startups 00:00:00
Working with Groups 00:00:00
Creating Static Set 00:00:00
Creating Dynamic Set 00:00:00
Combining Sets 00:00:00
Controlling Sets With Parameters 00:00:00
Dashboard: The Startup Quadrant 00:00:00
Dashboard Tricks 00:00:00
Section Recap 00:00:00
Quiz 00:00:00
Advanced Table Calculations
Section Intro 00:00:00
Project Brief: Coal Terminal Utilization Analysis 00:00:00
Creating Multiple Joins in Tableau 00:00:00
Calculated Fields vs Table Calculations 00:00:00
Creating Advanced Table Calculations 00:00:00
Saving a Quick Table Calculation 00:00:00
Specifying Direction of Computation 00:00:00
Writing your own Table Calculations 00:00:00
“Adding a Second Layer Moving Average “ 00:00:00
Quality Assurance For Table Calculations 00:00:00
Trendlines for Power-Insights 00:00:00
Creating a Storyline 00:00:00
Executive Report is Ready 00:00:00
Quiz 00:00:00
Advanced Data Prep + Analytics In Tableau
Section Intro 00:00:00
Project Brief: Retail Sector Forecasts 00:00:00
Building Box Plots in Tableau 00:00:00
Analysing Box Plots 00:00:00
Working with Large Data Sources 00:00:00
Pivot & Split 00:00:00
What Does Real World Retail Look Like? 00:00:00
Primary Use Case For Data Source Filters 00:00:00
Trendlines 00:00:00
Data Prep Exercise 00:00:00
Advanced Timeseries Blending 00:00:00
Calculating Sales Per Capita 00:00:00
Forecasting in Tableau 00:00:00
How to Present a Storyline 00:00:00
Quiz 00:00:00
Creating Animations in Tableau
Section Intro 00:00:00
Project Brief: World Health Trends 00:00:00
Editing Blending Relationships 00:00:00
Building the Visualisation 00:00:00
Adding Animation 00:00:00
Manually Sorting Blended Data 00:00:00
Leaving a Trail in your Animations 00:00:00
Finalizing the Dashboard 00:00:00
Quiz 00:00:00
Level Of Detail Calculations (LOD)
Section Intro 00:00:00
Project Brief: Regional Profit Analysis 00:00:00
Preparing the workbook 00:00:00
Aggregation and Granularity (refresher) 00:00:00
LOD Calculations Intuition 00:00:00
LOD Type 1: INCLUDE 00:00:00
Understanding ATTR() in Tableau 00:00:00
LOD Type 2: EXCLUDE (Part 1) 00:00:00
LOD Type 2: EXCLUDE (Part 2) 00:00:00
Multiple fields in an LOD Calculation 00:00:00
LOD Type 3: FIXED 00:00:00
Finalizing the Visualization 00:00:00
Quiz 00:00:00
DATA SCIENCE WITH PYTHON
Lesson 00 - Course Overview
0.1 Course Overview 00:00:00
Lesson 01 - Data Science Overview
1.1 Introduction to Data Science 00:00:00
1.2 Different Sectors Using Data Science 00:00:00
1.3 Purpose and Components of Python 00:00:00
1.4 Quiz 00:00:00
1.5 Key Takeaways 00:00:00
Lesson 02 - Data Analytics Overview
2.1 Data Analytics Process 00:00:00
2.2 Knowledge Check 00:00:00
2.3 Exploratory Data Analysis(EDA) 00:00:00
2.4 EDA-Quantitative Technique 00:00:00
2.5 EDA – Graphical Technique 00:00:00
2.6 Data Analytics Conclusion or Predictions 00:00:00
2.7 Data Analytics Communication 00:00:00
2.8 Data Types for Plotting 00:00:00
2.9 Data Types and Plotting 00:00:00
2.10 Knowledge Check 00:00:00
2.11 Quiz 00:00:00
2.12 Key Takeaways 00:00:00
Lesson 03 - Statistical Analysis and Business Applications
3.1 Introduction to Statistics 00:00:00
3.2 Statistical and Non-statistical Analysis 00:00:00
3.3 Major Categories of Statistics 00:00:00
3.4 Statistical Analysis Considerations 00:00:00
3.5 Population and Sample 00:00:00
3.6 Statistical Analysis Process 00:00:00
3.7 Data Distribution 00:00:00
3.8 Dispersion 00:00:00
3.9 Knowledge Check 00:00:00
3.10 Histogram 00:00:00
3.11 Knowledge Check 00:00:00
3.12 Testing 00:00:00
3.13 Knowledge Check 00:00:00
3.14 Correlation and Inferential Statistics 00:00:00
3.15 Quiz 00:00:00
3.16 Key Takeaways 00:00:00
Lesson 04 - Python Environment Setup and Essentials
4.1 Anaconda 00:00:00
4.2 Installation of Anaconda Python Distribution (contd.) 00:00:00
4.3 Data Types with Python 00:00:00
4.4 Basic Operators and Functions 00:00:00
4.5 Quiz 00:00:00
4.6 Key Takeaways 00:00:00
Lesson 05 - Mathematical Computing with Python (NumPy)
5.1 Introduction to Numpy 00:00:00
5.2 Activity-Sequence it Right 00:00:00
5.3 Demo 01-Creating and Printing an ndarray 00:00:00
5.4 Knowledge Check 00:00:00
5.5 Class and Attributes of ndarray 00:00:00
5.6 Basic Operations 00:00:00
5.7 Activity-Slice It 00:00:00
5.8 Copy and Views 00:00:00
5.9 Mathematical Functions of Numpy 00:00:00
5.10 Assignment 01 00:00:00
5.11 Assignment 01 Demo 00:00:00
5.12 Assignment 02 00:00:00
5.13 Assignment 02 Demo 00:00:00
5.14 Quiz 00:00:00
5.15 Key Takeaways 00:00:00
Lesson 06 - Scientific computing with Python (Scipy)
6.1 Introduction to SciPy 00:00:00
6.2 SciPy Sub Package – Integration and Optimization 00:00:00
6.3 Knowledge Check 00:00:00
6.4 SciPy sub package 00:00:00
6.5 Demo – Calculate Eigenvalues and Eigenvector 00:00:00
6.6 Knowledge Check 00:00:00
6.7 SciPy Sub Package – Statistics, Weave and IO 00:00:00
6.8 Assignment 01 00:00:00
6.9 Assignment 01 Demo 00:00:00
6.10 Assignment 02 00:00:00
6.11 Assignment 02 Demo 00:00:00
6.12 Quiz 00:00:00
6.13 Key Takeaways 00:00:00
Lesson 07 - Data Manipulation with Pandas
7.1 Introduction to Pandas 00:00:00
7.2 Knowledge Check 00:00:00
7.3 Understanding DataFrame 00:00:00
7.4 View and Select Data Demo 00:00:00
7.5 Missing Values 00:00:00
7.6 Data Operations 00:00:00
7.7 Knowledge Check 00:00:00
7.8 File Read and Write Support 00:00:00
7.9 Knowledge Check-Sequence it Right 00:00:00
7.10 Pandas Sql Operation 00:00:00
7.11 Assignment 01 00:00:00
7.12 Assignment 01 Demo 00:00:00
7.13 Assignment 02 00:00:00
7.14 Assignment 02 Demo 00:00:00
7.15 Quiz 00:00:00
7.16 Key Takeaways 00:00:00
Lesson 08 - Machine Learning with Scikit–Learn
8.1 Machine Learning Approach 00:00:00
8.2 Steps 1 and 2 00:00:00
8.3 Steps 3 and 4 00:00:00
8.4 How it Works 00:00:00
8.5 Steps 5 and 6 00:00:00
8.6 Supervised Learning Model Considerations 00:00:00
8.7 Knowledge Check 00:00:00
8.8 Scikit-Learn 00:00:00
8.9 Knowledge Check 00:00:00
8.10 Supervised Learning Models – Linear Regression 00:00:00
8.11 Supervised Learning Models – Logistic Regression 00:00:00
8.12 Unsupervised Learning Models 00:00:00
8.13 Pipeline 00:00:00
8.14 Model Persistence and Evaluation 00:00:00
8.15 Knowledge Check 00:00:00
8.16 Assignment 01 00:00:00
8.17 Assignment 01 00:00:00
8.18 Assignment 02 00:00:00
8.19 Assignment 02 00:00:00
8.20 Quiz 00:00:00
8.21 Key Takeaways 00:00:00
Lesson 09 - Natural Language Processing with Scikit Learn
9.1 NLP Overview 00:00:00
9.2 NLP Applications 00:00:00
9.3 Knowledge check 00:00:00
9.4 NLP Libraries-Scikit 00:00:00
9.5 Extraction Considerations 00:00:00
9.6 Scikit Learn-Model Training and Grid Search 00:00:00
9.7 Assignment 01 00:00:00
9.8 Demo Assignment 01 00:00:00
9.9 Assignment 02 00:00:00
9.10 Demo Assignment 02 00:00:00
9.11 Quiz 00:00:00
9.12 Key Takeaway 00:00:00
Lesson 10 - Data Visualization in Python using matplotlib
10.1 Introduction to Data Visualization 00:00:00
10.2 Knowledge Check 00:00:00
10.3 Line Properties 00:00:00
10.4 (x,y) Plot and Subplots 00:00:00
10.5 Knowledge Check 00:00:00
10.6 Types of Plots 00:00:00
10.7 Assignment 01 00:00:00
10.8 Assignment 01 Demo 00:00:00
10.9 Assignment 02 00:00:00
10.10 Assignment 02 Demo 00:00:00
10.11 Quiz 00:00:00
10.12 Key Takeaways 00:00:00
Lesson 11 - Web Scraping with BeautifulSoup
11.1 Web Scraping and Parsing 00:00:00
11.2 Knowledge Check 00:00:00
11.3 Understanding and Searching the Tree 00:00:00
11.4 Navigating options 00:00:00
11.5 Demo3 Navigating a Tree 00:00:00
11.6 Knowledge Check 00:00:00
11.7 Modifying the Tree 00:00:00
11.8 Parsing and Printing the Document 00:00:00
11.9 Assignment 01 00:00:00
11.10 Assignment 01 Demo 00:00:00
11.11 Assignment 02 00:00:00
11.12 Assignment 02 demo 00:00:00
11.13 Quiz 00:00:00
11.14 Key takeaways 00:00:00
Lesson 12 - Python integration with Hadoop MapReduce and Spark
12.1 Why Big Data Solutions are Provided for Python 00:00:00
12.2 Hadoop Core Components 00:00:00
12.3 Python Integration with HDFS using Hadoop Streaming 00:00:00
12.4 Demo 01 – Using Hadoop Streaming for Calculating Word Count 00:00:00
12.5 Knowledge Check 00:00:00
12.6 Python Integration with Spark using PySpark 00:00:00
12.7 Demo 02 – Using PySpark to Determine Word Count 00:00:00
12.8 Knowledge Check 00:00:00
12.9 Assignment 01 00:00:00
12.10 Assignment 01 Demo 00:00:00
12.11 Assignment 02 00:00:00
12.12 Assignment 02 Demo 00:00:00
12.13 Quiz 00:00:00
12.14 Key takeaways 00:00:00
Project 1
Project 1 Stock Market Data Analysis 00:00:00
Project 1 Demo 00:00:00
Project 2
Project 02 00:00:00
Main project 02 00:00:00
PYTHON BASICS
Lesson 00 - Course Overview
0.1 Introduction 00:00:00
0.2 Offerings 00:00:00
0.3 Course Objectives 00:00:00
0.4 Course Overview 00:00:00
0.5 Target Audience 00:00:00
0.6 Course Prerequisites 00:00:00
0.7 Need of Python 00:00:00
0.8 Python vs. Rest Other Languages 00:00:00
0.9 Value to the Professionals 00:00:00
0.10 Value to the Professionals (contd.) 00:00:00
0.11 Value to the Professionals (contd.) 00:00:00
0.12 Lessons Covered 00:00:00
0.13 Conclusion 00:00:00
Lesson 01 - Introduction to Python
0.1 Introduction 00:00:00
1.2 Objectives 00:00:00
1.3 An Introduction to Python 00:00:00
1.4 Features of Python 00:00:00
1.5 The History of Python 00:00:00
1.6 Releases 00:00:00
1.7 Installation on Ubuntu-based Machines 00:00:00
1.8 Installation on Windows 00:00:00
1.9 Demo-Install and Run Python 00:00:00
1.10 Demo-Install and Run Python 00:00:00
1.11 Example of a Python Program 00:00:00
1.12 Modes of Python 00:00:00
1.13 Batch Script Mode 00:00:00
1.14 Demo-Run Python in the Batch Mode 00:00:00
1.15 Demo-Run Python in the Batch Mode 00:00:00
1.16 Interpreter Mode 00:00:00
1.17 Demo-Run Python in the Interpreter Mode 00:00:00
1.18 Demo-Run Python in the Interpreter Mode 00:00:00
1.19 Indentation in Python 00:00:00
1.20 Indentation in Python (contd.) 00:00:00
1.21 Writing Comments in Python 00:00:00
1.22 Business Scenario 00:00:00
1.23 Quiz 00:00:00
1.24 Summary 00:00:00
1.25 Conclusion 00:00:00
Lesson 02 - Python Data Types
2.1 Python Data Types 00:00:00
2.2 Objectives 00:00:00
2.3 Variables 00:00:00
2.4 Types of Variables 00:00:00
2.5 Types of Variables-String 00:00:00
2.6 Types of Variables-Numeric Types 00:00:00
2.7 Types of Variables-Boolean Variables 00:00:00
2.8 Types of Variables-Boolean Variables (contd.) 00:00:00
2.9 Types of Variables-List 00:00:00
2.10 Adding Elements to a List 00:00:00
2.11 Accessing the Elements of a List 00:00:00
2.12 Types of Variables-Dictionary 00:00:00
2.13 Adding Elements to a Dictionary 00:00:00
2.14 Accessing the Elements of a Dictionary 00:00:00
2.15 Dictionary Methods 00:00:00
2.16 Dictionary Methods (contd.) 00:00:00
2.17 Operators 00:00:00
2.18 Opeators (contd.) 00:00:00
2.19 Logical Operators 00:00:00
2.20 Logical Operators (contd.) 00:00:00
2.21 Logical Operators (contd.) 00:00:00
2.22 Arithmetic Operations on Numeric Values 00:00:00
2.23 Order of Operands 00:00:00
2.24 Operators on Strings 00:00:00
2.25 Variables Comparison 00:00:00
2.26 Variables Comparison (contd.) 00:00:00
2.27 Variables Comparison (contd.) 00:00:00
2.28 Quiz 00:00:00
2.29 Summary 00:00:00
2.30 Conclusion 00:00:00
Lesson 03 - Control Statements
3.1 Introduction 00:00:00
3.2 Objectives 00:00:00
3.3 Pass Statements 00:00:00
3.4 Conditional Statements 00:00:00
3.5 Types of Conditional Statements 00:00:00
3.6 If Statements 00:00:00
3.7 If…Else Statements 00:00:00
3.8 If…Else If Statements 00:00:00
3.9 If…Else If…Else Statements 00:00:00
3.10 Nested If Statements 00:00:00
3.11 Demo-Use “If…Else” Statement 00:00:00
3.12 Demo-Use “If…Else” Statement 00:00:00
3.13 In Clause 00:00:00
3.14 Ternary Operators 00:00:00
3.15 Quiz 00:00:00
3.16 Summary 00:00:00
3.17 Conclusion 00:00:00
Lesson 04 - Loops
4.1 Introduction 00:00:00
4.2 Objectives 00:00:00
4.3 Loops in Python 00:00:00
4.4 Range Function 00:00:00
4.5 For Loop 00:00:00
4.6 For Loop (contd.) 00:00:00
4.7 While Loop 00:00:00
4.8 Nested Loop 00:00:00
4.9 Demo-Create Loops 00:00:00
4.10 Demo-Create Loops 00:00:00
4.11 Break Statements 00:00:00
4.12 Continue Statements 00:00:00
4.13 Quiz 00:00:00
4.14 Summary 00:00:00
4.15 Conclusion 00:00:00
Lesson 05 - Functions
5.1 Introduction 00:00:00
5.2 Objectives 00:00:00
5.3 Introduction to Functions 00:00:00
5.4 Creating Functions 00:00:00
5.5 Calling Functions 00:00:00
5.6 Arguments and Return Statement 00:00:00
5.7 Variable-Length Arguments 00:00:00
5.8 Variable-Length Arguments (contd.) 00:00:00
5.9 Recursion 00:00:00
5.10 Demo-Create a Function 00:00:00
5.11 Demo-Create a Function 00:00:00
5.12 Quiz 00:00:00
5.13 Summary 00:00:00
5.14 Conclusion 00:00:00
Lesson 06 - Classes
6.1 Introduction 00:00:00
6.2 Objectives 00:00:00
6.3 Classes 00:00:00
6.4 Objects 00:00:00
6.5 Creating a Basic Class 00:00:00
6.6 Accessing Variables of a Class 00:00:00
6.7 Adding Functions to a Class 00:00:00
6.8 Built-in Class Attributes 00:00:00
6.9 Init Function 00:00:00
6.10 Example of Defining and Using a Class 00:00:00
6.11 Example of Defining and Using a Class (contd.) 00:00:00
6.12 Demo-Create a Class 00:00:00
6.13 Demo-Create a Class 00:00:00
6.14 Quiz 00:00:00
6.15 Summary 00:00:00
6.16 Conclusion 00:00:00
Lesson 07 - Imports and Modules
7.1 Introduction 00:00:00
7.2 Objectives 00:00:00
7.3 Modules 00:00:00
7.4 Creating Modules 00:00:00
7.5 Using Modules 00:00:00
7.6 Using Modules (contd.) 00:00:00
Python Interpreter Module Search 00:00:00
Demo-Create and Import a Module 00:00:00
Namespace and Scoping 00:00:00
Dir() Function 00:00:00
Dir() Function (contd.) 00:00:00
Global and Local Functions 00:00:00
Reload a Module 00:00:00
Packages in Python 00:00:00
Quiz 00:00:00
Summary 00:00:00
Conclusion 00:00:00
2 STUDENTS ENROLLED

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