DATA ANALYSIS
This course provides a comprehensive introduction to data analysis, equipping you with the tools and techniques to extract, process, and interpret data to make informed decisions. By the end of the course, you’ll be able to analyze datasets, uncover trends, and communicate insights effectively.
What Will I Learn?
- Introduction to Data Analysis
- Understand the fundamentals of data analysis, including the process of collecting, cleaning, and interpreting data.
- Learn the different types of data (qualitative vs quantitative) and their role in analysis.
- Data Collection and Data Sources
- Learn how to gather data from various sources such as databases, spreadsheets, and external sources like APIs.
- Understand the importance of data quality and consistency in the analysis process.
- Data Cleaning and Preparation
- Master techniques for cleaning raw data, including handling missing values, correcting errors, and standardizing data formats.
- Learn how to preprocess data to make it ready for analysis.
- Exploratory Data Analysis (EDA)
- Learn how to explore data to identify trends, patterns, and outliers using statistical methods and visualizations.
- Discover how to use tools like Python (with libraries such as Pandas and Matplotlib), R, Excel, or Power BI for EDA.
- Data Visualization
- Understand the importance of visualizing data to uncover insights and communicate findings effectively.
- Learn how to create and interpret various types of charts, graphs, and dashboards using tools like Excel, Tableau, or Power BI.
- Statistical Analysis
- Learn the basics of statistics, including measures of central tendency (mean, median, mode), dispersion (variance, standard deviation), and correlation.
- Understand how to perform hypothesis testing, correlation analysis, and regression analysis.
- Data Modeling and Predictions
- Learn about predictive modeling techniques such as linear regression, decision trees, and machine learning algorithms.
- Understand how to apply these models to make data-driven predictions and decisions.
- Data Interpretation and Reporting
- Learn how to analyze and interpret data results to draw actionable insights.
- Understand how to present data findings in a clear and concise manner for stakeholders through reports or presentations.
- Advanced Data Analysis Techniques
- Explore advanced techniques such as time series analysis, clustering, and classification.
- Learn how to analyze complex datasets to uncover deeper insights.
- Data Analysis Tools
- Gain proficiency in popular data analysis tools like Excel, Python (with Pandas, NumPy, and Scikit-Learn), R, Power BI, and Tableau.
- Understand how to choose the right tool for different types of data analysis tasks.
- Data Ethics and Privacy
- Understand the ethical considerations involved in data analysis, including data privacy, transparency, and the responsible use of data.
- Problem-Solving with Data
- Learn how to apply data analysis techniques to solve real-world problems in areas like business, marketing, healthcare, finance, or social sciences.
Course Content
PYTHON ENVIRONMENTAL SETUP
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INTRODUCTION TO PYTHON
00:42 -
WHAT IS PHYTHON
05:17 -
JUPYTER NOTEBOOK
01:21 -
INSTALLING JUPYTER NOTEBOOK
06:46 -
RUNNING JUPYTER NOTEBOOK
08:45 -
COMMON JUPYTER NOTEBOOK
07:29