Data Analytics Using Python
Course: Data Analytics Using Python
The "Data Analytics using Python" program is designed to provide participants with a strong foundation in data science and analytics, using Python as the primary tool for implementation. This course aims to equip learners with the necessary skills to understand, analyze, and visualize data, empowering them to make data-driven decisions within their organizations.
Course Outcomes
Upon completion of this course, participants will:
- Build a solid foundation in the basic principles of data analytics and data science.
- Be equiped with the skills to acquire, clean, and preprocess data effectively.
- Learn how to conduct exploratory data analysis to extract meaningful insights from data.
- Be able to inspect and summarize data through aggregation and grouping.
- Use visualizations to communicate data insights clearly and effectively.
- Explain the role of data-driven decision-making in businesses and organizations.
- Be familiar with Python and its libraries such as Pandas, NumPy, and Matplotlib for data analysis and visualization.
Who It's For
This course is ideal for:
- Business and Data Analysts
- Programmers and software developers
- Data engineers
- Statisticians
- Data enthusiasts
- Entrepreneurs and Decision Makers
- Students and Researchers
- Anyone Interested in Data Analysis
Prerequisites
- Familiar with basic computer operations and using software applications.
Course Modules
1. Introduction to Data Analytics
- Understanding the foundations of data science
- Leveraging data-driven decision making
- Exploring data science workflow, roles, and tools
- Discovering real-world applications of data science
4. Statistics for Data Analytics
- Explain key statistics concepts for analysis such as central tendency, measures of spread, correlation
- Learn sampling fundamental concepts such as population, sampling frames, sampling errors, and learn how to properly sample data for analysis using simple random sampling (SRS).
- Implementing statistical concepts using Python.
2. Python for Data Analytics
- Introduction to Python as a programming language and its importance in data science.
- Python basics: variables, data types, and basic operations
- Control structures using loops, conditionals, and functions.
- Working with Python libraries
5. Data preparation and cleaning
- Identifying and handling duplicates in data set
- Ensuring data consistency across the dataset.
- Identifying and handling missing values
- Identifying and managing outliers in the dataset.
3. Inspecting data sets with Pandas
- Loading tabular data from files
- Inspecting data set using Pandas DataFrame
- Data validation and type transformation
- Data summarization using aggregation functions and groupby
6. Exploratory data analysis (EDA)
- Explain components of EDA: summarization and visualization
- Explore data using summarization techniques: descriptive statistics, frequency tables, distribution metrics, and aggregations
- Identifying the value of visual representation of data.
- Creating and interpreting univariate visualizations: histogram, box plot, bar plot.
- Learn multivariate visualizations: scatter plot, line plot, heatmaps
Flexible Training Options
At Datixlabs, we understand that every organization has unique training needs and scheduling constraints. That’s why we offer flexible training options to suit various learning environments and timeframes:
