Data Scientist is the hottest job of this decade. You can find almost anyone who sells online certification courses saying this. Do you have any idea how much the career opportunities in Data Science has grown until now? A report says the demand for Data Science professionals has increased by 400 percent.
High demand is not just the only reason to opt for a career as a Data Scientist. Becoming a Data Scientist doesn’t need strong mathematical skills as mistook by many. Anyone can learn to become one. This article will guide you on how to become a Data Scientist in 2021.
In this article, we’re going to see a complete guide on how to become a data scientist in 2021.
Role of a Data Scientist
Firstly, let’s discuss the role of a data scientist. There is some confusion floating around regarding the job role of a Data Scientist. Some find it difficult to differentiate between a Data Analyst, BigData Analyst, and a Data Scientist. A Data Scientist will be required to work with BigData along with other Data Science technologies such as Machine Learning. Also, a Data Scientist creates predictive models for performing analysis of data.
A Data Scientist is required to perform Custom Analysis, Statistical Analysis, Data Analysis, and Predictive Analysis. They also come up with predictive systems, auto lead scoring systems, and recommendation systems for the organizations they work. A Data Scientist also seeps through the information available in between the vast amount of data through Data Mining.
How to Become a Data Scientist in 2021?
Becoming a Data Scientist is not a tedious task. I will simplify the process of becoming a Data Scientist in the following steps.
Step 1: Get the required qualification
The educational requirements for a Data Scientist are given just below in detail. Make sure you meet the required credentials by seeking the appropriate higher degree or an online certification course. Many leading organizations provide a Data Science Online Course without any hassle. One can also watch some YouTube videos to get some basic ideas about Data Science and Data Scientists.
Step 2: Select an Area of Interest
There are several specializations when it comes to selecting a career as a Data Scientist. One could choose to become an NLP scientist, management analyst, market research analyst, or a data visualization specialist. One can also pursue a doctorate after a specific post-degree to become a specialist and Data Scientist. Ph.D. can be availed in areas of interest such as business solutions, data mining, data analytics, and enterprise science analytics.
Step 3: Get the required certification
Below are some of the certifications available in Data Science.
- Data Science Associate Certification
- Analytics Professional Certification
- SAS Data Science Certification
- Solutions Expert Certification
- Cloudera Associate Certification
- AWS BigData Certification
- Business Intelligence Certification
Educational Qualifications required to become a Data Scientist
As discussed previously, a Data Scientist will only be required to understand the basic concepts of mathematics. Preference is given to knowledge about statistics. Anyone with a degree, post-graduate degree, or Ph.D. can apply for the role of a Data Scientist.
But the process of understanding becomes easy for individuals with degrees in the following field of study: Mathematics, Applied Mathematics, Physics, Data Management, Computer Science, Economics, Information Technology, and Statistics.
There are Data Science Courses available online. Some well-known universities and organizations provide such Massive Online Open Courses (MOOCs) that facilitate learning from any part of the world.
Skills Required to Become a Data Scientist
Firstly, let’s see the skills you need to become a Data Scientist. Secondly, we’ll see the ways you can learn it such as platforms and institutions.
R and Python are the generally used programming languages. Python is preferred for performing statistical analysis. It is done so because it has high readability. Python is also applied in Machine Learning, Data Analysis, Data Visualization, and Data Analysis. R is used for solving problems arising in Data Science.
A Data Scientist is required to have familiarity in Statistical Analysis skills such as statistical tests, linear regression, distribution, probability, probability theory, and maximum likelihood estimators. Some of the analytical tools used for performing statistical analysis SAS, Hadoop, Spark, Hive, and Pig. A Data analyst is also required to perform Exploratory Data Analysis (EDA) and Time Series Analysis.
We might not need advanced math learning skills, but basic mathematical operational knowledge is required. One needs to brush off their basics in statistics, probability, matrix systems, and linear algebra. Having additional knowledge in Descriptive Statistics, Percentiles and Outliers, Bayes theorem, Cumulative Distribution Function (CDF), and Skewness of variables is welcome.
Machine Learning Skills
Machine Learning enables systems to perform and learn tasks by themselves without giving any explicit instructions. We train machines by employing several machine learning models and algorithms. These algorithms can be classified under supervised learning and unsupervised learning.
Ensemble Learning and Deep Learning
Ensemble Learning is the practice of Advanced Machine Learning. The significance of Deep Learning is that it helps solve the limitations involving Machine learning algorithms. Some of the Deep Learning skills that prove helpful are Neural Networks (Convolutional Neural Networks and Recurrent Neural Networks), RBM, and Autoencoders work. This is achieved through Deep Learning models such as Tensorflow and Kera.
Data Management is the process of performing data extraction, data transformation, data wrangling, and loading of data. It’s the process of cleaning the data in the warehouse. The data is then combined and data analysis is done to get actionable insights. Programming languages like Hadoop, Spark are used for this purpose.
Knowledge in Toolkits
Data Scientists are expected to know about applying tools such as MS Excel, R and Python tools, Hadoop, Spark, and Tableau.
Computer Vision and Data Visualization
Data Visualization is an additional skill required for one looking for a career in Data Science. Some of the applications used to acquire computer vision are Tableau, Google Charts, Kibana, and Data Wrapper.
Curiosity levels and Communication Skills
Last but not least, one needs to have an actual insatiable curiosity to learn Data Science. Data Scientist is a job role that needs constant updating. One also should be able to apply their storytelling skills to better communicate.