There is much being said about data science and becoming a data scientist, **Big Data**, the **Internet of Things**, **Deep Learning**, and **Artificial Intelligence**. The question is how do I become a data scientist and what is involved in learning data science.

There are 6 steps to learn Data Science^{4}:

- General Data Science
- Statistics
- Python
- R Programming
- Machine Learning
- Data Visualisation

## What is Data Science?

Data science is a field of study and practice that’s focused on obtaining insights from data. Practitioners of data science use programming skills, statistics knowledge, and machine learning techniques to mine large data sets for patterns that can be used to analyze the past or even predict the future.^{1}

Data science combines multiple fields, including statistics, scientific methods, artificial intelligence (AI), and data analysis, to extract value from data. Those who practice data science are called data scientists, and they combine a range of skills to analyze data collected from the web, smartphones, customers, sensors, and other sources to derive actionable insights.

Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. Analytic applications and data scientists can then review the results to uncover patterns and enable business leaders to draw informed insights.^{2}

## What Do Data Scientists Do?

In day to day work, data scientists are often responsible for everything that happens to data, from collecting it all the way through analyzing it and reporting on the results. Although every data science job is different, here’s one way to visualize the data science workflow, with some examples of typical tasks a data scientist might perform at each step.

It works like this:

**Capture data.**For example: pulling the data from a company database, scraping it from a website, accessing an API, etc.**Manage data.**For example: properly storing the data, and will almost always involve cleaning the data.**Exploratory Analysis.**For example: performing different analyses and visualizing the data in various ways to look for patterns, questions, and opportunities for deeper study.**Final Analysis.**For example: digging deeper into the data to answer specific business questions, and fine-tuning predictive models for the most accurate results.**Reporting.**For example: presenting the results of analysis to management, which might include writing a report, producing visualizations, and making recommendations based on the results of analysis. Reporting might also mean plugging the results of analysis into a data product or dashboard so that other team members or clients can easily access it.

All of that said, what data scientists do from day to day can vary tremendously, in no small part because *different companies make use of data science in different ways*.^{1}

The developing significance of data has, in turn, raised the importance of those people who handle these data. And that is why the position of a data scientist work is externally important and highly regarded in almost all places. Because the job of a data scientist work is relatively new, this role involves both business data analysis and technology. Therefore most people who fill this position have experience in both fields, making them a hybrid who knows the best of both worlds.

The importance of data and the need to gain important insights from them has led to some organizations investing in not just one data scientist work but a team that shares the responsibility for the same. The main reason why companies invest in a team as opposed to an individual is that the data scientist’s skill set may vary, and these might not be present in a single person.^{3}

## References

^{1} Custer, Charlie. “What is Data Science?” 2022. *dataquest.io*. https://www.dataquest.io/blog/what-is-data-science/.

^{2} “What Is Data Science?”. 2022. *oracle.com*. https://www.oracle.com/data-science/what-is-data-science/.

^{3} “Data Scientist Work | What Are The Responsibilities & Qualities”. 2016. *EDUCBA*. https://www.educba.com/data-scientist-work/.

^{4} “Learn To Be A Data Science Ninja – The Easy Way”. 2021. *Chi-Squared Innovations*. https://www.chi2innovations.com/blog/resources/ecourses/learn-data-science-ninja-easy-way/.

## Additional Reading

Andrade, Frank. “How To Self Study All The Technical Stuff You Need For Data Science”. 2022. *Medium*. https://towardsdatascience.com/how-to-self-study-all-the-technical-stuff-you-need-for-data-science-62e4b8b8152f.

In this article, I’ll show you the 5 steps I followed to learn the technical stuff needed for data science.

Egwu, Florence. “UNDERSTANDING THE MATHEMATICAL CONCEPTS OF DATA SCIENCE: A Beginner’s Overview”. 2021. *Medium*. https://florence-egwu.medium.com/understanding-the-mathematical-concepts-of-data-science-a-beginners-overview-a89832c96775.

In this article, we would be walking through some of the concepts a beginner data scientist would need to start off their journey to the wonder world of data.

“Every Data Scientist Should Know The Basics Of Linear Algebra”. 2020. *Medium*. https://towardsdatascience.com/basics-of-linear-algebra-for-data-science-9e93ada24e5c.

**Linear algebra** is a field of mathematics that is widely used in various disciplines. The field of **data science** also leans on many different applications of linear algebra. This does not mean that every data scientist needs to have an extraordinary mathematical background, since the amount of math you will be dealing with depends a lot on your role. However, a good understanding of linear algebra really enhances the understanding of many **machine learning algorithms**. Foremost, to really understand **deep learning algorithms**, linear algebra is essential. This article introduces the most important basic linear algebra concepts, and shows two relevant data science applications of linear algebra.

Hughes, Owen. “Employers are desperate for data scientists as demand booms”. 2022. *ZDNet*. https://www.zdnet.com/article/employers-are-desperate-for-data-scientists-as-demand-booms/.

Hunter, Madison. “A Q&A Guide To The Most Common Questions About How To Get A Job In Data Science”. 2022. *Medium*. https://towardsdatascience.com/a-q-a-guide-to-the-most-common-questions-about-how-to-get-a-job-in-data-science-a35523929750.

Getting a job in data science is hard.

Not only do you have to master a difficult mix of programming, mathematics, data analysis, and graphic design, but you also have to compete against hundreds of candidates with similar qualifications, abilities, and reasons for being there.

With such an interest in this exploding tech field, hundreds of free resources are available online to provide answers to all of your questions about data science. However, are any of them providing definitive answers that have been checked by others in the industry for accuracy?

With hundreds of resources to sift through, it can be tough to get a short, simple answer to a question. That’s where this guide comes in — to provide you with the basic information you need to answer all your burning questions about getting a job in data science.

⭐ “Kaggle: Your Machine Learning And Data Science Community”. 2022. *kaggle.com*. https://www.kaggle.com/.

Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community published data & code. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.

Malas, Meghan. “The value of a data science degree, as told by Microsoft’s chief data scientist”. 2022. *Fortune*. https://fortune.com/education/business/articles/2022/05/26/the-value-of-a-data-science-degree-as-told-by-microsofts-chief-data-scientist/.

Juan M. Lavista Ferres learned to code when he was 8 years old, and a few decades later his childhood interest in programming and technology evolved into a fruitful career—including a current stint at Microsoft of more than 13 years.

Murtaza, Ali. “An Introduction To Mathematical Thinking For Data Science”. 2022. *Medium*. https://towardsdatascience.com/an-introduction-to-mathematical-thinking-for-data-science-f1214d2cc61d.

Why it’s important to be able to think mathematically as a data scientist — and a detailed example of how to do it

Singh, Anamika. “Why Data Scientists Are Increasingly Quitting Their Jobs: Lack Of Skills Or Different Expectations?” 2022. *Medium*. https://medium.com/codex/why-data-scientists-are-increasingly-quitting-their-jobs-lack-of-skills-or-different-expectations-7c39daea6404.

*Despite the increasing demand for data scientists with handsome salaries, many professionals quit their jobs. Is it a lack of skills or different expectations?*

“What Is Data Science? A Complete Guide. | Built In”. 2022. *builtin.com*. https://builtin.com/data-science.

“What Is Data Science: Tutorial, Components, Tools, Life Cycle, Applications – JavaTPoint”. 2022. *javatpoint.com*. https://www.javatpoint.com/data-science.

## Videos

⭐ I suggest that you read the entire reference. Other references can be read in their entirety but I leave that up to you.