Data Science Using Python

Data science using Python is a powerful and popular approach due to Python’s extensive libraries and tools designed for data analysis, visualization, machine learning, and more. Here’s a guide to get you started

About the program

In our 6-month Data Science course focusing on Python, you’ll receive a comprehensive education designed to equip you with both foundational knowledge and advanced skills in the field. The curriculum begins with a thorough grounding in Python programming, emphasizing its application in data manipulation, visualization, and statistical analysis using libraries such as NumPy, Pandas, and Matplotlib. As you progress, you’ll delve into machine learning techniques, including supervised and unsupervised learning algorithms, regression, classification, and clustering methods.

Throughout the course, practical learning takes center stage with hands-on projects and case studies that mirror real-world scenarios. These projects will challenge you to clean and preprocess messy datasets, engineer features that enhance predictive performance, and develop models that provide actionable insights. You’ll also explore advanced topics such as deep learning, where you’ll build and train neural networks using frameworks like TensorFlow and PyTorch, and generative AI techniques like GANs and VAEs, enabling you to create synthetic data or generate new content.

Expert instructors and mentors will guide you through each stage, offering personalized feedback and support to refine your skills and deepen your understanding. Additionally, the course includes collaborative learning opportunities through peer interaction, group projects, and discussions that foster a community of practice and enhance your problem-solving abilities.

By the end of the program, you’ll have developed a robust portfolio of projects that demonstrate your proficiency with Python as a tool for data science, ready to showcase your abilities in a competitive job market or advance your career in analytics.

What Will You Learn?

Data Analysis & Visualization

Learn how to analyze data and present your findings in a compelling way

Statistical Methods

Understand and apply statistical techniques to data analysis.

Machine Learning

Get hands-on experience with machine learning algorithms and techniques.

Big Data Technologies

Learn about the tools and technologies used to handle large datasets.

Programming Skills

Enhance your programming skills in languages such as Python and R.

Domain Knowledge

Specialize in finance, healthcare, or marketing to apply data science effectively.

Data Science Course Curriculum

Week 1: Introduction to Data Analytics

  • Overview of Data Analytics
  • Importance in various industries
  • Career paths and opportunities

Week 2: Excel for Data Analytics

  • Data entry and formatting
  • Basic formulas and functions
  • Pivot tables and charts

Week 3: Introduction to SQL

  • Database concepts
  • Basic SQL queries (SELECT, WHERE, JOIN)
  • Data manipulation (INSERT, UPDATE, DELETE)

Week 4: Data Visualization with Excel and Introduction to Python

  • Creating visualizations in Excel
  • Basic Python programming
  • Using Jupyter Notebooks

Week 5: Advanced Excel Functions

    • Advanced formulas and functions
    • Data analysis tools (Solver, Analysis ToolPak)
    • Macros and VBA basics

Week 6: Advanced SQL

    • Complex queries
    • Subqueries and CTEs
    • Indexes and performance optimization

Week 7: Python for Data Analysis

    • Introduction to Pandas and NumPy
    • Data manipulation and cleaning
    • Basic data visualization with Matplotlib and Seaborn

Week 8: Data Wrangling and Transformation

    • Handling missing data
    • Data transformation techniques
    • Combining and merging datasets

Week 9: Data Visualization with Python

    • Advanced visualization techniques
    • Customizing plots with Matplotlib and Seaborn
    • Interactive visualizations with Plotly

Week 10: Introduction to Tableau

    • Tableau interface and basics
    • Creating dashboards
    • Advanced visualization techniques

Week 11: Reporting and Presentation Skills

    • Effective communication of data insights
    • Creating reports and presentations
    • Storytelling with data

Week 12: Power BI for Data Visualization

    • Introduction to Power BI
    • Data modeling and DAX
    • Creating interactive dashboards

Week 13: Descriptive and Inferential Statistics

    • Measures of central tendency and dispersion
    • Probability distributions
    • Hypothesis testing

Week 14: Introduction to Machine Learning

    • Machine learning concepts and workflow
    • Supervised vs. unsupervised learning
    • Model evaluation and selection

Week 15: Regression Analysis

    • Linear regression
    • Multiple regression
    • Model diagnostics and performance metrics

Week 16: Classification Techniques

    • Logistic regression
    • Decision trees
    • Model evaluation metrics

Week 17: Introduction to Big Data

    • Big data concepts
    • Overview of Hadoop and Spark
    • Use cases and applications

Week 18: Working with Spark

    • Introduction to PySpark
    • Data processing with Spark
    • Machine learning with Spark MLlib

Week 19: Cloud Computing for Data Analytics

    • Introduction to cloud platforms (AWS, Azure, GCP)
    • Data storage and processing in the cloud
    • Using BigQuery and other cloud-based tools

Week 20: Advanced Python Libraries

    • Advanced data manipulation with Pandas
    • Time series analysis
    • Geospatial data analysis

Week 21: Capstone Project Proposal

  • Identifying a real-world problem
  • Data collection and preprocessing
  • Project planning and milestones

Week 22: Capstone Project Development

  • Data analysis and visualization
  • Model building and evaluation
  • Iterative improvements

Week 23: Special Topics in Data Analytics

  • Data ethics and privacy
  • Emerging trends in data analytics
  • Industry case studies

Week 24: Capstone Project Presentation and Course Wrap-up

  • Finalizing the capstone project
  • Presenting findings to peers and instructors
  • Course review and next steps in career

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Who Can Apply for the Course?

Roles That Data Scientists Can Fulfill

Data Analyst

Analyzes data to extract actionable insights and support decision-making processes.

Machine Learning Engineer

Develops and implements machine learning models and algorithms.

Data Engineer

Designs, builds, and maintains data pipelines and infrastructure

Business Intelligence Analyst

Uses data to help businesses make informed decisions and strategies.

Research Scientist

Conducts advanced data research and experiments to develop new methodologies.

Quantitative Analyst

Applies mathematical models to solve financial and risk management problems.

Skill Covered

Python Programming

Big Data Technologies

NLP

Data Wrangling

Statistical Analysis

SQL

Data Ethics and Privacy

Predictive Modeling

Machine Learning

Data Mining

A/B Testing

Data Visualization

Deep Learning

Data Engineering

Tools Covered

Frequently Asked Questions

6 months, depending on the course you choose. Part-time and full-time options are available to fit your schedule.

Most courses offer flexible formats, including online, on-campus, and hybrid options, allowing you to choose the one that best suits your needs.

While some courses require basic knowledge of programming and statistics, others are designed for beginners. Check the prerequisites for each course before applying.

Yes, you will receive a certificate of completion or a degree, depending on the course you choose. These credentials can enhance your resume and career prospects.

Many course providers offer financing options, including installment plans, scholarships, and employer-sponsored programs. Check with the course provider for specific details.

Data science skills are in high demand across various industries. Completing these courses can open up new career opportunities, lead to higher salaries, and provide job security.

Frequently Asked Questions

The duration varies from 2 months to 9 months, depending on the course you choose. Part-time and full-time options are available to fit your schedule.

Most courses offer flexible formats, including online, on-campus, and hybrid options, allowing you to choose the one that best suits your needs.

While some courses require basic knowledge of programming and statistics, others are designed for beginners. Check the prerequisites for each course before applying.

Yes, you will receive a certificate of completion or a degree, depending on the course you choose. These credentials can enhance your resume and career prospects.

Many course providers offer financing options, including installment plans, scholarships, and employer-sponsored programs. Check with the course provider for specific details.

Data science skills are in high demand across various industries. Completing these courses can open up new career opportunities, lead to higher salaries, and provide job security.

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