Python for Data Science PG Internship (Self Paced+ Live)
Blended
Fee: ₹1,500
This program is designed for Postgraduate students who aim to develop advanced technical skills in Python and Data Science while fostering entrepreneurial thinking. The internship offers a hands-on, structured curriculum that integrates Python programming, data manipulation, statistical analysis, data visualization, and foundational entrepreneurship concepts. By the end of this internship, students will have the technical proficiency to analyze complex datasets, visualize data insights, and solve real-world problems, alongside the entrepreneurial mindset to identify opportunities and innovate solutions.
Objectives
This internship aims to equip students with essential Python programming and Data Science skills to analyze, interpret, and visualize data while fostering an entrepreneurial mindset to identify opportunities and innovate solutions.
To provide a comprehensive understanding of Data Science concepts and their real-world applications.
To equip students with essential Python programming skills required for data analysis and visualization.
To enable students to apply statistical techniques for data interpretation.
To foster entrepreneurial thinking, encouraging students to recognize opportunities and innovate solutions.
To prepare students for industry roles by enhancing problem-solving, critical thinking, and project execution skills.
What Will You Learn
Core Data Science Concepts: Explore the significance of Data Science, roles of a Data Scientist, and the industry landscape. Python Essentials: Develop proficiency in Python programming, covering variables, data types, loops, functions, and data structures like lists, dictionaries, and tuples. Data Manipulation and Analysis: Gain expertise in using libraries like NumPy and Pandas for data handling, transformation, and cleaning. Data Visualization: Create impactful visualizations using Matplotlib and Plotly to derive meaningful insights. Statistics for Data Science: Apply statistical methods for data analysis, including probability distributions, hypothesis testing, and linear algebra. Entrepreneurship Skills: Understand the fundamentals of entrepreneurship, types of enterprises, opportunity recognition, and the characteristics of successful entrepreneurs.
Skills you will gain
Curriculum
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Python for Data Science Introduction
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1.1 Intro What is Data Science?
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1.2 Real Life Examples with Applications
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1.3 Data Scientist Job roles and responsibilities
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1.4 The Most Promising Job of the 21st Century
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1.5 Machine Learning vs. Data Science vs. AI
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Python Essentials
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2.1 Python Introduction
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2.2 Follow this Installation Guide of Anaconda Navigator
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2.3 Jupyter Notebook Interface
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2.4 How to Open, Save and Rename a file
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2.5 Variables and Data Types
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2.6 Python Strings
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2.7 Loops (Conditional)
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2.8 Loops (Iterative while)
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2.9 Loops (Iterative For)
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2.10 Python Functions (Built in, Lambda, User defined functions)
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2.11 Lists
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2.12 Tuples
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2.13 Sets
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2.14 Dictionaries
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Data Manipulation and Data Analysis Tools
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3.1 Numpy Arrays in Python
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3.2 Three Dimensional Arrays Indexing and slicing
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3.3 Matrices
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3.4 Pandas in Python
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3.5 Importing CSV, Excel Files
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3.6 Exporting CSV file
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Python Data Analysis, Statistics
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4.1 Basic Descriptive Statistics with Numpy and Applying Statistical Functions on Matrices
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4.2 Linear Algebra with NumPy
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4.3 Numpy Random Numbers
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4.4 Probability Distributions using NumPy
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4.5 Normality Test with SciPy
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Advanced Python - Pandas
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5.1 Pandas Data Frames
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5.2 Querying Data in Pandas
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5.3 Data Aggregation with Pandas Data Frames Aggregation with Pandas Data Frames
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5.4 Concatenating and Appending Data Frames
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5.5 Joining Data Frames
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5.6 Statistics with Pandas Data Frames
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5.7 Handling Missing Data
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5.8 Pivot Tables
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5.9 Plotting in 5.9 Plotting in PandasPandas
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Data Visualialization in Python
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6.1 The Matplotlib Package
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6.2 Basic Matplotlib Plots
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6.3 Logarithmic Plots
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6.4 Scatter Plots
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6.5 Three Dimensional Plots
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6.6 Lag Plots
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6.7 Autocorrelation Plots
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6.8 Plot.ly
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Entrepreneurship Skill
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7.1 Introduction to the concept of enterprise
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7.2 Examples of Real world enterprises
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7.4 Discussion on young entrepreneurs
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7.5 Characteristics of entrepreneurs
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7.6 Advantages and Disadvantages of entrepreneurship
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7.7 Jobs Vs Entrepreneurship
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7.8 Types of Entrepreneurships
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8.1 Exploring ideas: Starting with your own idea
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8.2 Using pre-existing ideas
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8.3 Sources of ready ideas and schemes
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8.4 Market survey -How to gather information
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8.5 Secondary study and reports
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8.6 Primary study
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8.7 Identifying and analyzing competitors and their products
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8.8 Identifying your target markets: Identifying who will buy, geography
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8.9 Customer types - tools and templates to shortlist market segment
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9.1 Identifying and Mitigating Risks
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9.2 Legal and Regulatory Challenges
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9.3 Dealing with Failure and Setbacks
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10.1 Ideate and Plan Your Business
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10.2 Secondary Market Research
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10.3 Developing Market Strategy
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10.4 Financial Forecast
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10.5 Elevator Pitch
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Course SLM
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2 Live Sessions
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Final Project
Instructor
Dr. Priti Maheshwary
Professor Future Skills AcademyPresently working at Future Skill Academy. Involved in Computer Science and Engineering for over 20 years in higher education and training. Her career has included various software development projects, teaching, research and administrative roles. She enjoys teaching and looking into how to improve student learning experience. Published around 50 research papers in refereed journals and conferences, 7 book chapters, 6 Patents. 8 PhD thesis completed under her guidance in the field of Internet of Things, Smart Cities, Ubiquitous Computing, Wireless Sensor Network, VANET, Image Processing specialized in Satellite Images, AI/ML & Deep Learning, and Cyber Security. I have also done more than 10 projects in the field of Research and Consultancy.