Why Do You Need to Become a Data scientist?

data scientist

Data science is spreading like wildfire. This is creating a plethora of lucrative job opportunities. According to an IBM report, 2020 has nearly 2.7 million jobs in data analysis and other data science-related roles. This is an opportunity for job seekers and growth seekers to get into a fast-growing career.

Data science is evolving at a great speed. Harvard Business Review has called Data Scientist as the sexiest job of the 21st century, making data science jobs highly desirable among people.

What do data scientists do? 

Data scientists are analytical decision-makers who help companies make data-driven decisions. This requires them to have an in-depth understanding of statistics, mathematics, and machine learning. Data scientists work in collaboration with other business functions. They collect, process, and analyze data to arrive at a conclusive result and predict trends.

Responsibilities of a data scientist

Collect data – The increasing use of mobile apps, social media, and other digital interactive services has led to a deluge of data. Data scientists collect streaming and recorded data from a number of places including cloud databases, large excel sheets, PDFs, CSV files, and other forms of storage.

  1. Clean data – The data obtained in the above step is raw and highly unstructured. Data scientists turn this data into a usable form, which can be later used for analysis and other tasks. Nearly 90% of data analysts and data scientists spend their time cleaning data.
  2. Analyze data – A data scientist uses sophisticated statistical techniques like linear and logistical regression, ANOVA, hypothesis testing, etc. to analyze data.
  3. Visualization – Data scientists present the analyzed data in the form of graphs and charts, which are easily understandable by people in other teams.
  4. Building a model –The last yet the most significant part of a data scientist’s work is building a machine learning model. This is the hardest and the most impactful part of a data scientist’s work. As a data scientist, you build predictive models.

 Skills to become a data scientist 

  1. Statistics – It is the foundation of data science. Descriptive and inferential statistics are essential to know to excel in data science. Concepts like probability distribution, measures of central tendency and dispersion, population sampling are essential to work better in data science.
  2. Computer science/programming – Data scientists use R and Python to execute all data-related operations including analysis. R and Python are both used globally, so a comprehensive understanding of these languages is essential for doing well in data science in all parts of the world. In addition to programming, a data scientist is well –versed in arrays, matrices, data structures, and other essential technical aspects.
  3. Machine learning – This is the hardest part of data science. You are expected to know all frequently used machine learning algorithms including K-nearest, random trees, SVM, decision trees, etc. Data scientists build prediction/ forecasting models frequently.
  4. Critical thinking – Data scientists are problem solvers and work on the most pressing challenges of an organization. This requires a high level of critical thinking to understand projects and business problems and propositions. Solving business problems is important.

Get a data science certification

 Taking a certification always helps, especially for entry-level data science professionals. Professional certifications like Data Science Council of America (DASCA)’s ABDA (Associate Big Data Analyst) prove your expertise in fundamental data science skills essential to working as an analyst. Lack of concrete data science processes across the industry and parameters to assess the skill of professionals makes certifications an easy assessment tool for employers.

IBM and Dell EMC offer entry-level vendor-neutral data science certifications for aspiring data scientists. SAS, Microsoft, and Cloudera also offer certifications, but unlike IBM and Dell EMC certifications, which are platform agnostic, these are platform dependent.

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