WEEKLY SCHEDULE OF TRAINING

Week 1
Introduction
 Course Introduction
 Course Applications
 What is function
 using built-in functions
 create functions
 loops
Week 2
Introduction to Data Science and Python
 Introduction to Specialization
 Data Science
 Jupyter Notebook
 Auditing Learners
 Python Function
 Python Types and Sequences
 Python More on Strings
 Python Demonstration: Reading and Writing CSV
 Python Dates and Times
 Advance Python Objects, map()
 Advanced Python Lambda and List Comprehensions
 Advanced Python Demonstration: The Numerical Python Library (Numpy)
Week 3
Basic Processing with Panadas
 The series Data Structure
 Querying a Series
 The Data Frame Data Structure
 Data Frame Indexing and Loading
 Querying a Data Frame
 Indexing Data frames
 Missing Values
Week 4
Advance Python Pandas
 Merging Data frames
 Pandas Idioms
 Group by
 Scales
 Pivot Tables
 Data Functionality
1st monthly test
Week 5
Statistical Analysis in Python and Project
 Distributions
 Binomial Distribution
 Uniform Distribution
 Skewness
 kurtosis
Week 6
Principal of Information Visualization
 Chi square Distribution
 Tools for Thinking about Design
 Institute/Work ethics (For further detail
Week 7
Basic Charting
 Introduction
 Matplotlib Architecture
 Basic Plotting with Matplotlib
 Scatterplots
 Line Plots
 Bar Charts
Week 8
Charting Fundamentals
 Subplots
 Histograms
 Box Plots
 Heatmaps
2nd monthly test
Week 9
Applied Visualization
 Plotting with Pandas
 Seaborn
 Becoming an Independ Data Scientist
Week 10
Fundamentals of Machine Learning
 Introduction to Scikit Learn
 Key Concepts in Machine Learning
 Python Tools for Machine Learning
 An example Machine Learning Problem
 Examining the Data
 K-Nearest Neighbors Classification
Week 11
Supervised Machine Learning-Part 1
 Introduction to Supervised Machine Learning
 Overfitting and Underfitting
 Supervised Learning: Datasets
 K-Nearest Neighbors: Classification
 Multi-Class Classification
 Cross-Validation
 Freelancing continued…… Week 12
Evaluation
 Model Evaluation and Selection
 Confusion Matrices & Basic Evaluation Metrics
 Classifier Decision Functions
 Multi-Class Evaluation
 Model Selection: Optimizing Classifier
Week 13
Overview of the Previous Weeks & Mid Term Examination
Week 14
Supervised Machine Learning-Part 2 Freelancing
 Overview of Mid-Term Examination
 Naïve Bayes Classifier
 Random Forests
 Decision Trees
 Precision-recall
 Deep Leaning
 Dimensionality Reduction and Manifold Learning
 Clustering
 Conclusion
 Introduction to Freelancing
 Case Study
Week 15
Networks
 Networks: Definition and Why we Study them
 Network Definition and Vocabulary
 Node and Edge Attributes
 TA Demonstration: Loading Graphs in NetworkX
 Freelancing continued……
Week 16
Network Connectivity Freelancing
 Clustering Coefficient
 Distance Measures
 Connected Components
 Network Robustness
 Demonstration: Simple Network Visualization in NetworkX
 Freelancing concepts, how to start, step by step process from account opening to taking orders and contract signing etc.
 Freelancing platforms
4th monthly test
Week 17
Influence Measures and Network Centralization Freelancing Job Search & Entrepreneurial Skills(Job Search)
 Degree and Closeness Centrality
 Betweenness Centrality
 Basic Page Rank
 Scaled Page Rank
 Hubs and Authorities
 Centrality Examples
 Freelancing (Get some small projects of General Topics) Job market & job search
 Job related skills.
 Interpersonal skills
 Communication skills
Week 18
Network Evolution Job Search & Entrepreneurial Skills (CV Building)
 Preferential Attachment Model
 Small World Networks
 Closeness centrality
Session on CV Building.
 How to make notable CV.
 Dos and Don’ts of CV making
Week 19
Network Clustering
 Predicting Flight delays
 Identifying Nodes and Edges
 Highest Degree Nodes
 Lowest Degree Nodes
 Visualization
 Freelancing sites and starting actual work been started
Week 20
Employable Project/Assignment (6 weeks (i.e. 21-26) in addition of regular classes.
 Guidelines to the Trainees for selection of students employable project like final year project (FYP)
 Assign Independent project to each Trainee
 A project based on trainee’s aptitude and acquired skills.
 Designed by keeping in view the emerging trends in the local market as well as across the globe.
 The project idea may be based on Entrepreneur.
 Leading to the successful employment.
 The duration of the project will be 6 weeks
 Ideas may be generated via different sites such as:
https://1000projects.org/
https://nevonprojects.com/
https://www.freestudentprojects.com/
https://technofizi.net/best-computerscience-and-engineering-cse-projecttopics-ideas-for-students/
 Final viva/assessment will be conducted on project assignments.
 At the end of session the project will be presented in skills competition
 The skill competition will be conducted on zonal, regional and National level.
 The project will be presented in front of Industrialists for commercialization
 Project-1
5th monthly test
Week 21
Working with Text in Python
 Introduction to Text Mining
 Handling Text in Python
 Regular Expressions
 Demonstration: Regx with Pandas and Named Groups
 Internationalization and Issues with Non-ASCII Characters
Session on Self-Employment
 How to start a Business.
 Requirements (Capital, Human,Physical etc.)
 Benefits/Advantages of selfemployment
 Case Study
Week 22
Basic Natural Language Processing Job Search/ Entrepreneurial skills(General Overseas Employment)
 Basic Natural Language Processing
 Basic NLP tasks with NLTK
 Advanced NLP tasks with NLTK
 Institute/Work ethics (For further detail please see Annexure-II at the end)
 Session on General Overseas Employment opportunities
 Job search Avenues.
 Visa Processes and other necessar requirements.
 Immigration Information (Legal age requirements, Health Certificate, Police Clearance &Travel Insurance
Week 23
Classification of Text Job search/ Entrepreneurial skills (one country case)
 Text Classification
 Identifying Features from Text
 Naïve Bayes Classifiers
 Learning Text Classifiers in Python
 Sentiment Analysis
 Trade specific Job Prospects and Earning levels.
 Country Specific Labor laws, entry and exit requirements (Legal age requirements, Health Certificate, Police Clearance & Travel Insurance etc.
Week 24
Topic Modeling Job search/ Entrepreneurial skills (Two country Case)
 Semantic Text Similarity
 Topic Modeling
 Information Extraction
Selection of another country of destination (Gulf Countries, Malaysia, South Korea etc.) focusing on
I. Trade specific Job Prospects and Earning levels.
II. Country Specific Labor laws, entry and exit requirements (Legal age requirements, Health Certificate, Police Clearance & Travel Insurance etc.)
Week 25
 Revision and Q & A’s
Week 26
 Project-2 Finalization (Completion) Display
 Final Assessment
Final Assessment