Are you interested in learning how to analyze data using Python? This comprehensive course is designed to teach you all the skills you need to become a proficient data analyst.
You will start by learning the basics of Python programming, including data types, loops, functions, and more. From there, you will learn about data wrangling, cleaning, and preprocessing techniques that are necessary for effective data analysis. You will also explore how to use popular libraries such as NumPy, Pandas, and Matplotlib to visualize and manipulate data.
Next, you will learn about statistical analysis techniques such as hypothesis testing, regression analysis, and ANOVA. You will also explore machine learning algorithms, including linear regression, decision trees, and random forests.
Throughout the course, you will work on hands-on projects that will give you practical experience in data analysis. By the end of the course, you will have a strong foundation in data analysis using Python and will be able to apply your skills to real-world problems.
- “Big Data Analytics: Concepts and Tools”
In this course, you will learn about the fundamentals of big data analytics and the tools used to analyze large datasets. You will start by learning about the characteristics of big data and how it differs from traditional data. You will also explore the challenges associated with big data, such as storage, processing, and analysis.
Next, you will learn about the different types of big data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics. You will also explore the tools used to analyze big data, such as Hadoop, Spark, and NoSQL databases.
Throughout the course, you will work on projects that will give you practical experience in big data analytics. By the end of the course, you will have a strong foundation in big data analytics and will be able to apply your skills to real-world problems.
- Important terms in statistics
- Overviews of the most important statistical methods
