Data Analytics Course

What Will You Learn?

• Data Collection and Preparation: Learn how to gather, clean, and preprocess data from diverse sources to ensure accuracy and reliability in analysis.
• Exploratory Data Analysis (EDA): Master techniques for exploring and understanding datasets, including summary statistics, distribution analysis, and outlier detection.
• Data Visualization: Gain proficiency in using visualization tools and techniques to present data effectively and uncover meaningful insights.
• Statistical Analysis: Understand basic statistical concepts and methods for analyzing data, including measures of central tendency, variability, and hypothesis testing.
• Regression Analysis: Learn how to use regression models to understand relationships between variables and make predictions based on data.
• Classification and Clustering: Explore classification algorithms to categorize data into distinct groups and clustering techniques to identify patterns within datasets.
• Time Series Analysis: Understand how to analyze time-series data to identify trends, seasonality, and anomalies.
• Machine Learning Fundamentals: Introduction to machine learning concepts and algorithms, including supervised and unsupervised learning techniques.
• Data Wrangling and Feature Engineering: Develop skills in data wrangling to prepare data for analysis and feature engineering to create relevant features for predictive modeling.
• Practical Projects: Apply learned concepts and techniques to real-world datasets through hands-on projects, gaining valuable experience in solving data analytics challenges.