What you'll be learning
The Data Science with Python course is tailored for aspiring data scientists, covering Python basics, data manipulation, and advanced analytics using Pandas, NumPy, Matplotlib, and Seaborn. Gain expertise in data visualization, statistical foundations, machine learning algorithms, and hands-on Scikit-learn practice
The program also includes Power BI dashboards, Big Data concepts with Hadoop and Spark, and real-world projects like sentiment analysis and image recognition, preparing you to tackle real-world data science challenges effectively.
Module 1: Python Fundamentals
Learn the basics of Python programming, including syntax, data types, variables, and control statements. This module lays the foundation for working with Python in data science and explores its applications across industries.
Module 2: Data Structures and Collections
Explore Python’s built-in data structures, including lists, tuples, sets, and dictionaries. Understand their usage, manipulation techniques, and performance considerations in data analysis.
Module 3: Data Manipulation with Pandas
Dive into the Pandas library to work with data structures like Series and DataFrames. Learn data cleaning, transformation, and handling missing values while mastering techniques for efficient data manipulation.
Module 4: Data Visualization
Master data visualization with libraries like Matplotlib, Seaborn, and Plotly. Create impactful visual representations, including bar charts, scatter plots, heatmaps, and dashboards, to convey insights effectively.
Module 5: Statistics and Probability
Build a solid understanding of statistical concepts like measures of central tendency, dispersion, hypothesis testing, and probability distributions, essential for data analysis and machine learning.
Module 6: Machine Learning Fundamentals
Learn supervised and unsupervised learning techniques, including regression, classification, clustering, and ensemble models. Hands-on labs with Scikit-learn provide practical implementation skills.
Module 7: Data Wrangling and Preprocessing
Understand data cleaning, handling outliers, encoding, feature scaling, and data integration. This module prepares data for advanced analytics and machine learning models.
Module 8: Power BI and Data Visualization Tools
Learn to create interactive dashboards and reports using Power BI. Explore visualization libraries like ggplot2 and Tableau to design dynamic and professional-grade data presentations.
Module 9: Big Data and Spark
Understand distributed computing with Hadoop and Spark. Gain hands-on experience in RDDs, Spark SQL, and machine learning on Spark for handling large-scale datasets.
Module 10: Real-World Projects
By completing this course, you will:
- Master Python programming and essential libraries for data manipulation and visualization (Pandas, NumPy, Matplotlib, etc.).
- Learn to clean, preprocess, and manipulate real-world datasets.
- Understand key statistical concepts and hypothesis testing for data analysis.
- Implement machine learning models (regression, classification, clustering) using Scikit-learn.
- Create dynamic visualizations and dashboards with tools like Power BI, Tableau, and Plotly.
- Learn Hadoop and Spark for distributed data processing and analysis.
- Work on real-world data science projects to build a strong portfolio.
- Prepare for roles in Data Science, Data Analysis, and Business Intelligence.
