9 Must-have skills you need to become a Data Scientist

Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, skepticism of existing assumptions — to uncover solutions to business challenges.

1. Education

Data scientists are highly educated — 88% have at least a Master’s degree and 46% have PhDs — and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. To become a data scientist, you could earn a Bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills you need to process and analyze big data.

After your degree programme, you are not done yet. The truth is, most data scientists have a Master’s degree or Ph.D and they also undertake online training to learn a special skill like how to use Hadoop or Big Data querying. Therefore, you can enroll for a master’s degree program in the field of Data science, Mathematics, Astrophysics or any other related field. The skills you have learned during your degree programme will enable you to easily transition to data science.

2. R Programming

In-depth knowledge of at least one of these analytical tools, for data science R is generally preferred. R is specifically designed for data science needs. You can use R to solve any problem you encounter in data science. In fact, 43 percent of data scientists are using R to solve statistical problems. However, R has a steep learning curve.

3. Python Coding

Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists. This is why 40 percent of respondents surveyed by O’Reilly use Python as their major programming language.

Because of its versatility, you can use Python for almost all the steps involved in data science processes. It can take various formats of data and you can easily import SQL tables into your code. It allows you to create datasets and you can literally find any type of dataset you need on Google.

4. Hadoop Platform

Although this isn’t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial. A study carried out by Crowd Flower on 3490 Linkedin data science as the second most important skill for a data scientist with 49% rating.

As a data scientist, you may encounter a situation where the volume of data you have exceeds the memory of your system or you need to send data to different servers, this is where Hadoop comes in. You can use Hadoop to quickly convey data to various points on a system. That’s not all. You can use Hadoop for data exploration, data filtration, data sampling and summarization.

5. SQL Database/Coding

Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL. SQL (structured query language) is a programming language that can help you to carry out operations like add, delete and extract data from a database. It can also help you to carry out analytical functions and transform database structures.

You need to be proficient in SQL as a data scientist. This is because SQL is specifically designed to help you access, communicate and work on data. It gives you insights when you use it to query a database. It has concise commands that can help you to save time and lessen the amount of programming you need to perform difficult queries. Learning SQL will help you to better understand relational databases and boost your profile as a data scientist.

6. Apache Spark

Apache Spark is becoming the most popular big data technology worldwide. It is a big data computation framework just like Hadoop. The only difference is that Spark is faster than Hadoop. This is because Hadoop reads and writes to disk, which makes it slower, but Spark caches its computations in memory.

Apache Spark is specifically designed for data science to help run its complicated algorithm faster. It helps in disseminating data processing when you are dealing with a big sea of data thereby, saving time. It also helps data scientist to handle complex unstructured data sets. You can use it on one machine or cluster of machines.

Apache spark makes it possible for data scientists to prevent loss of data in data science. The strength of Apache Spark lies in its speed and platform which makes it easy to carry out data science projects. With Apache spark, you can carry out analytics from data intake to distributing computing.

7. Machine Learning and AI

A large number of data scientists are not proficient in machine learning areas and techniques. This includes neural networks, reinforcement learning, adversarial learning, etc. If you want to stand out from other data scientists, you need to know Machine learning techniques such as supervised machine learning, decision trees, logistic regression etc. These skills will help you to solve different data science problems that are based on predictions of major organizational outcomes.

Data science needs the application of skills in different areas of machine learning. Kaggle, in one of its surveys, revealed that a small percentage of data professional are component in advance machine learning skills such as Supervised machine learning, Unsupervised machine learning, Time series, Natural language processing, Outlier detection, Computer vision, Recommendation engines, Survival analysis, Reinforcement learning, and Adversarial learning.

Data science involves working with large amounts of data sets. You may want to be familiar with Machine learning.

8. Data Visualization

The business world produces a vast amount of data frequently. This data needs to be translated into a format that will be easy to comprehend. People naturally understand pictures in forms of charts and graphs more than raw data. An idiom says “A picture is worth a thousand words”.

As a data scientist, you must be able to visualize data with the aid of data visualization tools such as ggplot, d3.js and Matplottlib, and Tableau. These tools will help you to convert complex results from your projects to a format that will be easy to comprehend. The thing is, a lot of people do not understand serial correlation or p values. You need to show them visually what those terms represent in your results.

Data visualization gives organizations the opportunity to work with data directly. They can quickly grasp insights that will help them to act on new business opportunities and stay ahead of competitions.

9. Teamwork

A data scientist cannot work alone. You will have to work with company executives to develop strategies, work product managers and designers to create better products, work with marketers to launch better-converting campaigns, work with client and server software developers to create data pipelines and improve workflow. You will literally have to work with everyone in the organization, including your customers.

Essentially, you will be collaborating with your team members to develop use cases in order to know the business goals and data that will be required to solve problems. You will need to know the right approach to address the use cases, the data that is needed to solve the problem and how to translate and present the result into what can easily be understood by everyone involved.