Data Analytics Using Python and SQL
This course provides a strong foundation in Data Analytics, covering key concepts and practical application of analytics techniques to real-world business challenges.
COURSE OUTCOMES
At the end of the course, students should be able to
- Establish your learning environment for mastering SQL and Python essentials for data analyzing.
- Create robust data engineering pipelines with Spark, using Python as the programming
- You'll learn popular data analysis models and tools, empowering you to tackle diverse analytical tasks effectively.
OBJECTIVES OF THE COURSE:
- Master sorting and filtering commands in SQL to effectively organize and extract data.
- Gain proficiency in essential Excel data tools such as Sorting, Filtering, Data Validation, and Data Importing.
- Gain a deep understanding of data collection and preprocessing techniques essential for tackling Linear Regression problems in Machine Learning.
COURSE SYLLABUS:
In this module, you'll get an overview of Data Analytics and understand the significance of Python and SQL in this field. You'll learn about the role of Python for data manipulation and analysis, as well as SQL's importance for querying and managing databases.
This module covers the basics of Python programming necessary for data analytics. You'll learn about fundamental concepts such as data types, variables, and control flow statements.
Additionally, you'll explore the Pandas library for data manipulation tasks like loading datasets, cleaning data, and performing basic transformations.
Here, you'll be introduced to Structured Query Language (SQL) and its role in data analytics. You'll start with the basics, learning how to write simple SQL queries to retrieve data from databases. Topics include SELECT statements, filtering and sorting data, and basic aggregation functions.
Building upon your foundational SQL knowledge, this module delves into more advanced SQL techniques. You'll learn about subqueries and Common Table Expressions (CTEs) for writing complex queries. Additionally, you'll explore window functions for performing advanced analytical operations.
Data visualization is a crucial aspect of data analytics, enabling you to communicate insights effectively. You'll learn how to generate various types of plots and customize them to convey your data effectively.
Exploratory Data Analysis (EDA) is the process of analyzing data sets to summarize their main characteristics. You'll learn statistical techniques to summarize data and perform univariate and bivariate analysis. EDA helps in understanding the underlying patterns and relationships within the data, laying the foundation for further analysis.