What I will learn?

• Introduction to Data Science:
• Fundamental concepts of data science and its applications.
• Overview of the data science lifecycle, including data collection, cleaning, analysis, and visualization.
• Statistics and Data Analysis:
• Essential statistical techniques for data analysis.
• Probability theory, inferential statistics, hypothesis testing, and regression analysis.
• Machine Learning:
• Introduction to machine learning algorithms and their applications.
• Supervised and unsupervised learning, including classification, regression, clustering, and dimensionality reduction.
• Big Data and Cloud Computing:
• Technologies and tools for handling and analyzing large datasets.
• Introduction to cloud computing platforms and big data frameworks like Hadoop and Spark.
• Data Visualization:
• Techniques for visualizing complex data to uncover insights and support decision-making.
• Tools and libraries for data visualization, such as Tableau, D3.js, and Matplotlib.
• Programming for Data Science:
• Programming languages and tools commonly used in data science, such as Python, R, and SQL.
• Hands-on experience with data manipulation, analysis, and visualization using these tools.