Total Duration: 30 hours

Average Lecture Segment: 10 minutes per lecture

Weekly Segment: 4 hours

Credits: 6 (1 credit = 5 hours)

Course Fee Details

Early Bird Registration Fee by 31 August 2024: ₹ 250

31 August 2024 to 5 September 2024: ₹ 500 (Use discount code – TD050924 and get upto 50% off )

Standard Registration Fee: ₹1000

Important Dates

• Course Starts on: 10 September 2024
• Completion Date: 15 November 2024

Course Overview:

“Data Analytics with Python: Foundations and Applications” is a comprehensive course designed to introduce participants to the fundamentals of data analysis using Python. It covers essential topics such as Python programming, data wrangling with pandas, data visualization with Matplotlib and Seaborn, statistical analysis, and basic machine learning techniques. Participants will gain hands-on experience through practical assignments and a real-world project in areas like Health, Business, Social Media, Finance, and more. This course is ideal for students, professionals, and anyone looking to harness the power of data science to drive decisions and innovations.

Mode of Course

• Online: The course will be delivered entirely online, allowing participants to learn at their own pace from anywhere.
• Recorded Lectures: Access pre-recorded video lectures and tutorials.
• Live Sessions: Participate in scheduled live Q&A sessions with instructors.
• Self-Paced Learning: Complete assignments, quizzes, and projects on your own schedule.
• Interactive Forums: Engage with peers and instructors through online discussion forums.

Course Objectives:

• Understand the basics of Python programming. 

• Learn to manipulate and analyze data using Python libraries. 

• Apply statistical methods to data analysis. 

• Visualize data using Python. 

• Gain hands-on experience through projects in health science, environmental science, business analytics, social science, medical science and many more.

Eligibility for Enrollment:

• Undergraduate students currently enrolled in any academic program. 

• Professionals seeking to enhance their data analysis skills using Python. 

• Individuals with a basic understanding of programming and a keen interest in data science. 

• Students from health science, environmental science, and related fields are looking to apply data analysis techniques in their studies and research. 

• Anyone interested in learning the fundamentals and applications of data analysis with Python 

Course Commitment

Spend 4 hours per week on recorded lectures, assignments, quizzes, reading materials, and projects to complete the course in 10 weeks.

Prerequisites:

To ensure that students can successfully complete the course, the following prerequisites are recommended: 

• Basic Computer Literacy: Familiarity with operating systems (Windows, macOS, or Linux). Basic knowledge of file management and using web browsers 

• Mathematics Fundamentals: Understanding of basic algebra, Familiarity with basic statistics concepts (mean, median, mode, standard deviation, probability) 

• Programming Knowledge: Basic understanding of programming concepts such as variables, loops, and conditionals. Prior experience with any programming language (e.g., C, C++, Java) is beneficial but not mandatory 

• Software Installation: Ability to install software on a computer, including Python and relevant libraries (instructions will be provided during the course) 

Course Outline

Module 1: Introduction to Python and Data Analysis (4 hours) 

• Introduction to Python programming 

• Setting up the Python environment (Anaconda, Jupyter Notebooks) 

• Basic Python syntax and data structures (lists, tuples, dictionaries) 

• Introduction to libraries: NumPy, pandas, Matplotlib, and Seaborn 

Module 2: Data Wrangling with Pandas (6 hours) 

• Introduction to pandas 

• Reading and writing data (CSV, Excel, JSON) 

• Data cleaning and preprocessing 

• Handling missing values 

• Data manipulation (filtering, sorting, grouping) 

• Merging and joining datasets 

Module 3: Data Visualization (6 hours) 

• Introduction to data visualization 

• Plotting with Matplotlib 

• Advanced visualization with Seaborn 

• Customizing plots (labels, legends, titles) 

• Creating subplots and complex visualizations 

Module 4: Statistical Data Analysis (4 hours) 

• Descriptive statistics 

• Probability distributions 

• Hypothesis testing 

• Correlation and regression analysis 

Module 5: Introduction to Machine Learning (6 hours) 

• Basics of machine learning 

• Supervised vs. unsupervised learning 

• Introduction to scikit-learn 

• Building and evaluating machine learning models (regression, classification) 

• Model validation and cross-validation techniques 

Module 6: Real-world Data Analysis Project (4 hours) 

• Project selection and planning 

• Data collection and cleaning 

• Exploratory data analysis 

• Model building and evaluation 

• Presenting findings 

Assessment Criteria and Certification

To successfully complete the self-paced course and earn the certification in Data Analysis with Python: Foundations and Applications, participants must meet the following assessment criteria:

(1) Module Quizzes:

• Quizzes will be administered at the end of each module to test comprehension and retention.
• Participants must score at least 70% on each quiz to progress to the next module.

(2) Hands-On Assignments:

• Practical assignments will be given throughout the course to apply theoretical knowledge.
• Assignments will be graded based on accuracy, completeness, and adherence to instructions.
• Participants must achieve a cumulative score of 75% or higher on all assignments.

(3) Final Project:

• A comprehensive final project will require participants to analyze a dataset using Python, incorporating techniques learned throughout the course.
• The project will be evaluated on the basis of problem definition, data preparation, analysis, interpretation of results, and presentation.
• Participants must score at least 80% on the final project to pass.

(4) Participation in Discussion Forums:

• Active participation in discussion forums is encouraged to foster collaborative learning and problem-solving.
• While this is not graded, engagement will be considered for overall course completion.

(5) Course Completion Certificate:

• Upon meeting the above criteria, participants will receive a certification of completion.
• The certificate will detail the skills acquired and the total hours of instruction and practical application completed.

By fulfilling these assessment criteria, participants will demonstrate their proficiency in data analysis with Python and be well-prepared to apply these skills in various professional contexts.

Course Experts

Our Data Analysis with Python certificate course is led by a team of distinguished experts in the field, ensuring a comprehensive and enriching learning experience:

• Dr. Mehar Chand: Department of Mathematics, Baba Farid College, Bathinda, INDIA
• Prof.(Dr.) Pooja: Professor & Associate Dean, Sharda University, UZBEKISTAN
• Dr. Aparna Kumar: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat INDIA
• Dr. Upinder Kaur: Department of Computer Science and Engineering, Akal University, Bathinda, INDIA
• Dr. Parijata Majumdar: Department of Computer Science and Engineering, Techno College of Engineering, Agartala, Tripura, INDIA

Terms and Conditions