How to become a Data Scientist

You must have heard a lot about Data Science – a highly demanded profession and which more and more professionals from other fields are joining.

Harvard Business Review published an article in which the economist and head of the financial sector of Google, Hal Varian, says that the sexiest career is that of a data scientist. 

Well, the reason for this statement is not far-fetched! Data scientists are a valuable asset for any company. Every company’s objective locally and internationally is to work towards increasing its competitiveness in the market. And these professionals help them to achieve this objective by making effective use of big data.

Also, it’s interesting to know that the data science career will keep thriving in the future because there will be even more increases in the level of data in companies.

So, if you’re considering delving into this profession, that’s a good idea. However, you need to be informed. To help you, this article contains important information you need to make a career as a Data Scientist.

First, who is a Data Scientist?

A Data Scientist is sometimes described as someone who can find a lump of gold in a large mountain of unstructured data. Unstructured data means that the data comes from various data sources such as e-mails, videos, photos, and social media. 

As Big-Data has become a trend in many industries, the demand for data scientists is increasing enormously. The core task of the Data Scientist is to structure and analyze large amounts of data to discover specific insights that are relevant to the organization for which he works. 

For instance, to be able to develop new competitive products or services and have a large market share, companies call on data scientists. And thus this professional will carry out an in-depth analysis of the massive data (Big Data) that the company has collected through various channels. 

This information generally concerns prospects, customers, and even employees of the company. The objective is to identify the various problems related to the company – in particular the market, marketing, customer loyalty, or human resources – to provide solutions.

The Data Scientist does this by using different technologies, or by developing technologies for this by writing algorithms. And therefore supplies this data as input to other disciplines in the organization, such as management or product development.

Essential skills you need as a data scientist

Data Scientists require skills such as:

• Programming skills: To extract, clean, and investigate data, data scientists use tools like R and Python. These programs, also usually create predictive models.
• Analytical and business skills: As a data scientist, you must have a good understanding of how the business works to add value to it, and in turn, have command of analysis, visualization, and database tools.
• Statistical and mathematical skills: These professionals work with numbers and outliers. Therefore, they must be able to have a good understanding and numerical intelligence, work in an orderly manner and understand statistical models.
• Communication skills: Data Scientists work with different departments and must communicate their findings to them, therefore, they must be excellent storytellers.

How do you become a data scientist?

Becoming a data scientist generally requires formal training. If you want to get started, here are some steps to follow.

1. Learn Advanced Mathematics

The first step toward becoming a data scientist is learning advanced mathematics. As Mathematics is important to the algorithms and analytics in data science, it’s essential to master topics in advanced mathematics such as Multivariable Calculus, Statistics, Linear Algebra, and Probability.

You can learn all these topics from the final year high-school mathematics textbook.

2. Know the algorithms 

As a data scientist, employers expect you to be proficient in algorithms as it’s needed for efficient programming.

To know the algorithms, start with learning the basics of machine learning algorithms, and their advantages and disadvantages.  Then, learn the deep learning algorithms that you can use in solving complex project that is based on computer vision in data science. And lastly, understand the NLP techniques as they’re useful when leveraging textual data.

3. Learn a programming language

Building algorithms used by data scientists from scratch in a programming language is quite complicated, but with the knowledge of programming languages such as Python and Scala, the implementation of the algorithms in data science will be easy.

If you want to learn the Python programming language, a free Python tutorial is provided on the Python site.

4. The Collection of data

Data collection is necessary for data science to make data-driven decisions, reliable research, and ensure quality assurance.  For the collection of data, you’ve to know different tools that are used in procuring data from local systems as CSV files and scraping data from websites.

With the knowledge of Query language or Python ETL pipelines,  you can manage data collection.

5. Data cleaning

After knowing how to collect data from various sources the next step is data cleaning. Data cleaning is all about filtering out unwanted data from the raw data to obtain the data fit for the work.

Data cleaning is very important as the raw form of data is messy and obtaining the needed with the use of some Python libraries such as Numpy and Panda, is important for a data scientist.

6. Start practicing

Knowledge increase when we put into practice what is learned. As you’re learning the basics of coding, practice what you are learning by building projects that answer interesting questions that will show your data science skills.

As a start, you don’t need to build complex projects. The important thing is to find engaging datasets, ask questions about the data, and then answer those questions with code. But as you progress, you can start building complicated projects.

Also, not only do building projects help you in improving your skills, but it’s also a way of building a portfolio you can show to potential employers.

7. Share your projects

There are lots of advantages to sharing your projects. By sharing your projects, your presentation skill will increase as you’ll be thinking about how best you should present it every time, it allows peers to view and provide feedback on it, and it also enables employers to view it.

There are different platforms you can share your data science projects on. One of them is Github.

In addition, you can also start a blog where you can discuss data science and programming, your data science journey, and more.

8. Connect with people

The next step after building an online presence is to start engaging with other data scientists both learners and experts. Engaging with other people in your field will help in increasing your knowledge, enhancing your profile, and helps in opening doors of opportunities.

You can do this through online platforms or in person. Such online platforms include Quora, reddit.com/r/datascience, and Kaggle. 

9. Keep learning

The more you learn, the more you earn. Companies are looking for data scientists who can find those vital insights that can reduce their spending or satisfy their customers. You too should apply that method by intensifying your learning to be under this category of Data Scientist.

Keep answering more complex questions and keep finding new questions to solve.

Where do data scientists work?

Data scientists work in organizations with a strong need to understand big data and make it useful for business operations. Data scientists work for technology companies, government, healthcare, energy companies, insurance companies, multinationals, and banks.  

A data scientist often works together with product owners, project leaders, managers, programmers, econometricians, data analysts, and business intelligence consultants.

Conclusion

Building a career in the field of data science is a challenge. However, with patience and dedication to learning every day, you will be a sought-after professional data scientist in no time.