Introduction to Data Science:
Overview of data science, its applications, and its role in solving real-world problems.
Programming Languages:
Hands-on training in essential programming languages like Python and R, which are widely used in data science for data manipulation, analysis, and visualization.
Data Manipulation and Exploration:
Techniques for collecting, cleaning, and transforming raw data using libraries like Pandas, NumPy, and SQL.
Exploratory Data Analysis (EDA) to understand data patterns, relationships, and anomalies.
Statistics and Probability:
Learning foundational statistics to derive meaningful insights from data.
Topics include descriptive statistics, probability theory, hypothesis testing, and A/B testing.
Data Visualization:
Creating data visualizations using tools like Matplotlib, Seaborn, and Tableau to convey data insights clearly.
Machine Learning:
An introduction to machine learning algorithms such as linear regression, classification, clustering, and decision trees.
Practical implementation of algorithms using libraries like Scikit-learn and TensorFlow.
Big Data and Tools:
Introduction to big data technologies like Hadoop, Spark, and NoSQL databases to handle and analyze large datasets.
