Data Analysis Curriculum

Data analysis is a rapidly growing field essential for making informed decisions and solving complex problems. This course is designed to introduce students to the basics of data analysis, including statistical concepts, data wrangling and cleaning techniques and data visualization.

Throughout the course, students will learn how to use programming languages such as Python and R to perform data analysis, as well as how to use tools such as SQL databases and NoSQL databases to store, retrieve, and organize data. They will also gain an understanding of big data technologies and their applications and the ethical considerations involved in working with data.

By the end of the course, students will have a solid foundation in data analysis and will be equipped with the skills and knowledge needed to apply these techniques in a variety of settings. Whether you are a student, a professional, or simply someone with an interest in data, this course is a great introduction to the exciting and rapidly growing field of data analysis.

The Data analysis curriculum covers the following topics:

1. Statistics: including probability, hypothesis testing, and regression analysis.
2. Data Wrangling and Cleaning: techniques for cleaning, transforming and aggregating data.
3. Data Visualization: techniques for creating meaningful and effective visual representations of data.
4. Data Storage and Management: concepts and techniques for storing, retrieving and organizing data.
5. Data Mining: techniques for discovering patterns and relationships in large datasets.
6. Big Data: technologies and architectures for processing and analyzing large datasets.
7. Data Ethics: including privacy, bias and transparency in data analysis.

Tools Needed

1. Programming languages: Python
2. Data Visualization Tools: Tableau
3. Data Wrangling and Cleaning Tools: Pandas, Numpy, OpenRefine
4. Data Storage and Management Tools: SQL databases such as MySQL, PostgreSQL, and NoSQL databases like MongoDB.
5. Big Data Tools: Apache Spark, Apache Hadoop, Apache Flink, etc.
6. Data Collaboration Tools: Jupyter Notebook, Google Colab, etc.

Deliverables (what you should be able to do after the classes):

1. Cleaned and preprocessed data
2. Data visualizations
3. Statistical analysis
4. Report summarizing your findings and implications.

Chat with us via WhatsApp to enrol.

Pro Tips:

What is the difference between data analysis, data analytics and data science?

Data analysis and data analytics are related by slite different concepts.

Data Analysis refers to the process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves exploring and visualizing data and identifying patterns, and relationships.

Data Analytics refers to the use of statistical methods and computational tools to perform data analysis and interpret data. It is a broader term that encompasses the entire process of working with data, from collecting and cleaning it to analyzing and visualizing it to making predictions or decisions based on the results.

In other words, data analysis is a step in the data analytics process, and data analytics is the entire process of working with data to extract insights and inform decision-making.

Data Science, on the other hand, is a broader field that encompasses data analytics and much more. Data Science involves the use of statistical and machine learning techniques, algorithms, and programming languages to solve complex data-related problems. It involves not only analyzing data, but also collecting and cleaning data, building predictive models, and creating visualizations.

Starting with Data Analysis will get you started in the data ecosystem. Please chat with us via WhatsApp to enrol.