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In this course, you will learn :
- How to use Python to perform predictive data analysis. Those who want to start a career as data analysts are the target audience.
- How to use statistics to extract useful insights from data that can be used to forecast future behaviour or patterns.
- All of the tools of the trade that data scientists use on a daily basis, such as NumPy, Pandas, Matplotlib, and Seaborn.
- How to extract meaningful insights from data, but you will also learn how to create stunning visualisations for use in reports.
- Exposed to a variety of datasets from real-world scenarios in order to become accustomed to working with any type of data.
- You will work on two real-world projects that demonstrate how data analysis techniques are used to generate revenue in the financial and advertising sectors.
Syllabus :
1. Numpy for Python
- What is NumPy?
- NumPy Array Creation
- NumPy Array Indexing
- Transposing of NumPy Array
- Processing NumPy Arrays
2. Pandas for Python
- What is Pandas?
- Series in Pandas
- DataFrame in Pandas
- Reindex Objects
- Select and Drop Entries in Series
- Select and Drop Entries in DataFrame
- Sort and Rank in Pandas
- Dealing with Missing Data
- Additional Functions and Features
3. Statistics for Data Analysis
- Statistical Features
- Probability Distributions
- Central Limit Theorem
4. Data Wrangling
- Dealing with CSV Files
- Merging Data
- Reshaping Data
- Mapping Data and Finding Duplicates
- Replace and Rename
- Finding Outliers in Data
- Data Grouping
- Data Aggregation
- Split-Apply-Combine Technique
5. Visualizing the Data
- Visualization Tools
- Histogram Plots
- Box Plots
- Regression Plots
- Heatmaps
- Scatter and KDE Plots
6. Data Scraping
- What is Scraping?
- Exploring Selenium
- Scraping Domains
