What are the skills required to be a Data Scientist?

Top Skills for Data Scientist - What are the skills required to be a Data Scientist?

In this article we are going to discuss the top skills that Data Scientist must learn and master to become productive at their work. These are the top skills required to be a successful Data Scientist in today's competitive world. Data science career requires experience in many technologies which includes programming, Math and other branches of science.

Data Science is a big field and it requires soft skills along with very good technical skills. Soft skills are required because only data scientist can understand actual requirement of client then translate into Mathematical problem. Finally solve this problem using various machine learning techniques and then communicate the results to client in dashboard formats. So, a lot of client interaction and technical work is needed to accomplish any data science project. So, these two skills are must to become productive data scientist.

In this article we are going to discuss technical and non-technical skills required to become a Data Scientist.

What are the skills required to be a Data Scientist?

There are many skills is required to become a Data Scientist who can work on the deep learning and artificial intelligence projects to deliver as per client expectations. There are many different types of technologies to learn and master. Many skills like project management, client management, team management and software project management is also required. Here in this article we will discuss only 10 top skills which is necessary to master to become a productive Data Scientist.

Here are the lists of top 10 skills that Data Scientist must learn and master:

1. Educational Qualification

The job of Data scientist is very technical and most of the data scientists are highly educated technical resource. If we see the statistics then we will find that 88% of the Data scientists have Master's degree while 46% have PhDs. A strong educational background is required to become productive data scientist as these jobs are highly technical and various Mathematical calculations are required. Programming skills along with Mathematical skills are rare and it requires a lot of effort to learn all these. It requires years of learning Maths and IT subject to become a Data scientist.

In this field most of the study required are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in all these subjects will give you required knowledge for data analysis. To become a Data scientist one can earn Bachelor's degree in Computer science, Social sciences, Physical sciences, Maths and Statistics. Degree in all these is not possible so Maths and Computer science is must.

There may graduates having good knowledge of Maths, Statistics and Computer science subjects are also working as Data Scientist in industry. What more matter is the theoretical and practical skills to achieve company's goal in Data Science. So, IT professionals from other field can also learn Data science if they spend time and study with heart.

Experience in Big Data technologies such as Hadoop, Hive, NoSQL, SQL databases are also required. Because most of the time Data scientists are interacting with the data stored on the Big Data cluster. So, you should have sold understanding of all these technologies.

2. Programming

There are many programming languages which can be used to develop application for data science. But the most import programming skills are R, Python, Spark, Hadoop SQL and AI/ML packages. So, lets see one be one.

a) R Programming

The R Programming language is one of most used programming language for machine learning. It comes with many packages and libraries which can be used for data science application development. R programming language is Mathematical processing framework which is designed for data science needs. About 43% of data scientists are using R programming language for developing data science project for their clients.

R programming language can be used to develop many different types of machine learning and deep learning programs. R programming language comes with thousands of libraries for developing machine learning applications. R Programming language is complex programming language as it comes with many libraries. So, you have to learn many library of R to work on the data science project.

The R programming language is complex and R learning has very steep learning curve. But with consistent effort and practice it can be learned. These days over 43% of data scientists are using R Programming language to solve machine learning problem. You can learn R Programming language at our R Programming tutorials section.

b) Python Coding

Python is now one of the most popular programming language for writing machine learning and deep learning program. It is used extensively by Data scientist around the world to develop, test and deploy their machine learning projects. Python is very powerful programming language it comes with many core libraries which can be used in developing tons of applications. According to the various surveys over 40% of Data scientists are using Python as main language for development of data science applications.

Python comes with many machine learning libraries such as TensorFlow, Keras, Numpy and others which help in developing data science applications much easier. To become a data scientist you must learn Python programming language. Check our Python Programming tutorials section to learn Python from scratch or for refreshing your Python programming skills.

c). Big Data Platforms (Hadoop Platform0

Big Data Platforms are heart of today's data centres and data science projects. Large scale data is stored and processed with the help of Hadoop platform, so knowledge of Big and Hadoop software components are necessary for data scientists. Apache Spark, Apache Hive, Apache Sqoop etc. are highly used platforms these days. If you don't have experience then you must learn all these.

Most of the time Data scientists interact with Hadoop Big Data platform for getting data for processing and finally saving the processed result back to the Hadoop Big Data lake. Data scientist must learn how to interact with various components of Hadoop Platform.

d) SQL Database/NoSQL/Coding

Experience in working with the SQL, NoSQL and other data sources are important. Most of the times you have get data from these data sources for data preparation and model training. Data scientist spends around 60% of their time on the data cleansing and data preparation tasks. These days most of the data are stored with the SQL, NoSQL and file formats. So, Data scientist must have skills to get data from these sources.

e). Apache Spark

Apache Spark is one of the best framework for processing large amount of data over distributed clusters and very fast speed. Apache Spark comes with the SparkML module which can be used for training data science model over distributed cluster. It also comes with RDD and Dataframe libraries which can be used for data preparation tasks at very large scale. So, data scientist must know Apache Spark programming in Python and/or Scala.

f) Machine Learning and AI

Strong experience of various machine learning techniques like neural networks, reinforcement learning, adversarial learning, etc is must for Data Scientist. You must have theoretical and practical experience on many industry proejcts.

Data scientist must have advanced 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
Adversarial learning

These techniques are extensively used in machine learning projects. Data Scientist work involves processing and training the model on the large scale data sets. So, one should have data cleansing, processing and data messaging skills also.

3. Statistics

The Statistics play very important role in machine learning and deep learning, but vey low percentage of data scientists learned statistics as part of regular course. So, it is necessary for the Data Scientists to learn core concepts of Statistics and practice with the statistics concepts. In Data science statics are used very extensively and in-depth knowledge is must for a Data scientist.

4. Machine Learning

There are many machine libraries such as TensorFlow, Keras, Cafee, Microsoft Cognitive Toolkit (Previously CNTK), PyTorch, Apache MXnet, DeepLearning4J, Theano, TFLearn, Torch, Cafee2, PaddlePaddle, DLib, Chainer, Neon, Lsagne, H2O.ai, PyLearn2, BigDL, Shogun, Apache SIGNA, Blocks, Mocha and many others. So, Machine learning engineer must learn as many libraries he/she can learn. But these days TensorFlow, SparkML, Keras and Scala SparkML is most important libraries. So, one must learn at least TensorFlow, SparkML, Keras and Scala SparkML.

Data scientist must learn all the algorithms of machine learning and deep learning. You will find more details at What are prerequisites of machine learning? and What should I learn for machine learning?

5. Linear Algebra and Calculus

Math is the heart of machine learning and all the business problems are translated into Mathematical problem, which later on solved using machine learning programming libraries. The Linear algebra gives algorithms, and calculus provides solutions for optimization the algorithm. So, outstanding knowledge and experience is must for a Data Scientist.

6. Data Visualization

Business users prefer the visualization of results in the form of chart, graphs and summarized statistics. It is also easy to understand the results in the form of visualization dashboard. So, Data scientist must have experience in various Data visualization tools and programming language.

One can learn data visualization tools such as ggplot, d3.js and Matplottlib, and Tableau. These tools will help Data scientist in presenting the result to business users in meaningful and attractive format.

7. Communication

Companies are looking for the Data scientist who can take ownership of complete project. Data scientist should be able to interact with the client to understand their requirement and also interact with the non-technical team. From various stockholders Data scientist must be able to clearly communicate about the problem to solve and final solution for particular problem.

8. Data Wrangling

This process is can be called as data wrangling, data munging, or data transformation, the part of the Data Science Process which requires data pre and post processing. This process is very huge and consumes around 60% time of Data scientist. So, this is one of the most import work that must be done properly and without any error. If you provide garbage to during training of the model you will get garbage output during prediction. So, Data scientist spends lots of time in this process. 0

The six core activities of data wrangling are Discovering, Structuring, cleaning, Enriching, Validating and Publishing. Data scientist must learn the various techniques used for data wrangling process.

9. Software Engineering

Software Engineering is the application of engineering concepts to develop software. In simple term Data scientist must have prior experience in software development using Java, Python, Scala, Apache Spark, C++ and such similar programming language. These days machine learning libraries are available for these high level programming language and strong programming experience in these programming language is must to have skill for a Data Scientist. If you don't have programming skills then you can start with the Java and Python programming languages.

10. Intellectual curiosity

Data Science is fast growing field with new innovations on regular basis. Data Scientist must desire to acquire more knowledge in this fast changing world and it is necessary to keep up with the pace. 1

One must read news/updates/technologies contents online on regular basis along with the latest books on data science.

Tutorials for Data Science