Core Data Science Syllabus Covered in Data Science Courses

A degree in data science offers multidisciplinary training that enables the course seekers to mine, process, transform and extract knowledge from large volumes of data including numbers, images, video, text, etc. Considering the wide applicability of data science, such courses comprise the data science syllabus that covers the concepts of mathematics, statistics, artificial intelligence, machine learning, cloud computing, big data, natural language processing, etc. The data science syllabus is crafted to provide the necessary knowledge for the overall professional development of the course takers as a data scientist, data analyst, big data specialist, artificial intelligence expert, business consultant, data architect, etc.

Read more – What is Data Science?

Essential Components of Data Science Syllabus

The data science syllabus across all platforms, be it classroom setting or online, remain similar, covering the core concept of the above-mentioned topics and projects. The key topics covered under core concepts of data science include –

Basics of Data Science and its Methodologies

This topic covers the basic concepts of data science and will help the course seekers identify in which situations it can be applied. Basics of data science and its methodologies include –

• An overview of data science and its methodologies.
• The use of data science methodologies and analyze the aspects
• Impart practical experience in applying a data science methodology
• Concepts of data cleaning, data mining, data labeling, data wrangling, and data visualization, among others.

Statistics for Data Science

Statistics for Data Science is the core of data science and the essential component of the data science syllabus. You will acquire the minimum statistical knowledge required to select, adapt and implement statistical methods for data analysis in relation to a particular problem. Statistics for Data Science will help you to –

• Know and apply different statistical techniques for the summary, description, and data visualization
• Learn the concepts of stochastic processes, statistical inference, and Bayesian methods, etc.
• Build and validate statistical models
• Develop the ability to model data acquisition processes in a probabilistic way, from both a frequentist and Bayesian approach
• Analyze real situations that allow gaining experience in the generation, test, and interpretation of statistical hypotheses

Must Read – Statistical Methods Every Data Scientist Should Know

Mathematics for Data Science covers the concepts of linear algebra, multivariable calculus, and probability of widespread use in data science, through concrete examples of common models or algorithms. With Mathematics for Data Science, you will –

• Understand the elementary notions of linear algebra for data science
• Know how to use, through relevant examples in machine learning, the elementary concepts of non-linear optimization methods
• Understand and apply the basic concepts of probability in the modeling framework
• Simple probability, probability distributions, matrix analysis, distributed algorithms, and optimization used in data science

Applied Machine Learning

You will learn to develop the necessary skills to apply learning methods to solve business problems, run evaluations, interpret their results and understand the requirements for scaling a machine learning problem from huge chunks of data. You will also learn –

• How and under what conditions to apply machine learning algorithms
• Understand and select the criteria and techniques for measuring the quality of a supervised learning algorithm
• Understand and apply collaborative filters for recommendation systems
• Understand and know the existing technology to scale machine learning solutions and manage large volumes of data

You might be interested in – Top 10 Machine Learning Algorithms for Beginners

Programming and Coding

You will develop the programming skills and knowledge necessary for the development of data science projects. Programming and Coding are essential components of the data science syllabus that will help you to –

• Know and use the basic tools for the development of products of data
• Develop Python programming skills for data science and usage of specialized Python libraries like NumPy, SciPy, Pandas, Matplotlib, etc.
• Develop skills and knowledge of programming in R for data science
• Learn about basic development tools: GIT, Docker, command line, Jupyter, text editors, and so on

Business Models and Strategies for Data Science

Learning data science strategies and building models allows the development of skills and knowledge necessary to establish and implement successful data science objectives within the business model of a company or organization. The objectives include –

• Understand the role of data science in business and how it can be used to achieve business objectives
• Recognize which data science techniques to use and ask questions relevant within a data science process
• Build the right team according to the data science needs of the company, and learn how to recruit and retain talent in the data science team
• Establish and measure key performance indicators that allow recognizing the processes that generate value in the company in the application of data science

Data Analysis   

Data analysis will indulge aspiring data scientists in hands-on experience with loading, visualizing, and modeling real data sets. The concept of data analysis covers the core techniques of data collection, filtering, and analyzing. You will also learn to draw conclusions from the data and structure data analysis reports using Data Visualization tools like Tableau, Spotfire, Qlikview, and Microsoft Power BI, etc.

Note: These data science topics are the major offerings in a particular course and may vary as per the course and course provider.

What Else is a Part of Data Science Syllabus?

The data science syllabus is vast and is not limited to the above inclusive. The course content becomes more specialized and in-depth with the level of the understanding of the course seekers. Some of these specialized topics covered under the data science syllabus include –

1. Data Engineering
2. Big data
3. Optimization Metaheuristics
4. Deep Neural Networks
5. Statistical Inference for Data Science
6. Multivariate Statistics for Data Science
7. Databases for Data Science
8. Natural Language Processing
9. Topological Data Analysis
10. Simulation and Monte Carlo Techniques
11. Programming with Hadoop and Spark
12. Multivariate Methods for Machine Learning
13. Deep Learning and Text Mining
14. Longitudinal Data Modelling
15. Time Series and Forecasting
16. Data Management and Data Warehousing
17. Applied Data Analytics
18. Data-Based Business Generation
19. Business Intelligence
20. Information Security
21. Data Science Consulting

Conclusion

If you aspire to become a data scientist then I hope this article helped you in understanding the inclusions of a data science course/degree. The data science syllabus is quite vast and comprises very in-depth topics. Depending on your professional aspirations and your level of expertise, you can pick the course that can help you to sharpen your existing skills. To help you with some good courses, I have a list that might interest you. Here you go –

• Data Science: Statistics and Machine Learning Specialization by Coursera
• Introduction to Statistics by Coursera
• Principles, Statistical and Computational Tools for Reproducible Data Science by edX
• Advanced Certification in Data Science and Artificial Intelligence by IIT Madras
• Data Science for Engineers by IIT Madras
• Data Science Methodology by IBM on Coursera
• Data Science Math Skills by Duke University on Coursera
• Data Science: Machine Learning from Harvard University on edX
• Intro to Data Science on Udacity
• Advanced Statistics for Data Science Specialization by Johns Hopkins University on Coursera