Course description

If you want to learn how to successfully manage and analyze data, this course is for you. Excel is a crucial tool for data analysis.

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• Formulas: Master the art of formula creation and use for computation, data manipulation, and job automation. Learning the fundamentals of mathematics, as well as conditional formulae and more complex functions, can help you streamline your processes.
• Pivot Tables: together with charts and statistical functions, are crucial for summarizing and extracting valuable insights from enormous datasets. Easily examine data trends, distributions, and patterns with the help of Pivot Tables, charts, and statistical tools.
• Python/R: Python and R are two of the most popular programming languages among data scientists due to their large libraries and flexibility.
• Basics, Functions, Strings: Learn the ropes of Python or R by diving into the language's foundational concepts, such as the syntax, functions, and strings. Data analysis and manipulation at a higher level will be easier to tackle after completing this session.
• Data Structures, Libraries (Pandas, Matplotlib, NumPy): Learn the fundamentals of data structures such as arrays, dictionaries, and lists. In addition, you will learn to use NumPy for numerical computation, Matplotlib for data visualization, and Pandas for data manipulation, so you can tackle difficult data problems.
• Tableau / PowerBI: This course will teach you how to use Tableau or Power BI to create visually appealing data presentations, which is an essential skill for any data scientist. Discover the power of visual analytics by mastering the art of creating dynamic data exploration and informative visualizations.
  Learning how to use your visual analytics tools to create a wide range of charts and graphs, as well as how to execute operations on data and computations, is essential if you want to draw useful conclusions from your data.
  Worksheet and Workbook Formatting, Advanced Chart Types: Elevate your data visualizations with advanced chart types and personalize your workbooks and worksheets for polished reports and presentations.
  
• Mathematics and Stats:Data scientists must possess a solid grasp of mathematics and statistics. The essential principles are covered in this unit.
• Mathematical Foundations:Learn the maths that support data science by delving into topics like algebra to comprehend linear models, calculus to solve optimization issues, and probability and statistics to understand and analyze data.
• Data Collection:Collecting and managing data efficiently is the backbone of any data-driven project. This module will guide you through SQL and NoSQL databases.
• SQL and NoSQL:Learn the differences between SQL (Structured Query Language) and NoSQL databases, and how to use them effectively to store, retrieve, and manage data.
• SQL Queries:Master SQL queries to perform operations on databases, including selecting, inserting, updating, and deleting data. You'll also learn to join tables, filter data, and execute complex queries to prepare data for analysis.
• Machine Learning:The core of data science is machine learning. The essential methods and strategies used in predictive modeling are introduced in this subject.
  Beginning with linear regression for predictive modeling and classification methods for data categorization, you will learn and use important Machine Learning algorithms. You will also learn how to evaluate models. Mastering the art of evaluating model performance using several measures and honing models to achieve higher accuracy is essential.