How to Become a Data Scientist in 2021?

In addition to covering how you can become a Data Scientist, we have also prepared a set of content that will help you truly understand the scope and career path that you can expect while pursuing the Data Science field.
The demand for Data Scientists has increased significantly over the years as more and more businesses are finding value in utilizing voluminous amounts of structured, unstructured, and semi-structured data generated by these enterprises and IoT.
Check out this video by Intellipaat on Data Science:

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Data Science Overview
In 2017, studies by NodeGraph showed that our digital universe had accommodated 2.75 zettabytes (ZB) of data. It is predicted that this will grow to 175 ZB, a ridiculously colossal amount in the global datasphere, by the year 2025, according to IDC. It shouldn’t even come as a surprise with the kind of online activity that exists today.
The Data Science domain deals with all these giant datasets, finding ways to make them useful and integrating them into real-world applications. Digital data is considered the oil of the 21st century with its multitude of business, research, and daily-life benefits. Everything from your social media posts to your most recent Google search is essential for Data Scientists in one way or the other at the end of the day.
The process of sifting through the wide gamut of data is a task that Data Scientists are trained specifically for. They are skilled in delivering critical insights, which in turn enable better decision-making. Most companies today boast of using some form of Data Science. The truth is that it is difficult to put everything in a box and specifically define it. In more general terms, Data Science encompasses the extraction of clean data from raw data and further goes on to the analysis of these datasets to make sense out of it or, in other words, generate valuable and actionable insights through visualization.
Although Data Science doesn’t have a clean life cycle with well-defined stages, there are, however, seven major stages in Data Science:
Data acquisitionData preparationData modelingEvaluation and interpretationModel deploymentMaintenanceOptimizationBut, what does a Data Scientist do? Following is the list of the common deliverables in Data Science:
Automation and decision-making (medical treatments, credit card approval, etc.)Classifications (important emails, spam, promotions, etc.)Recommendations (based on learned preferences about movies, restaurants, music, etc.)Forecasting (electricity demand, customer retention, revenue, etc.)Anomaly detection (fraud, malfunctioning equipment, disease, etc.)Pattern detection (financial market patterns, data mining, weather patterns, etc.)Recognition (text, voice, facial, etc.)Actionable insights (visualizations, reports, dashboards, etc.)Segmentation (e.g., demographic-based marketing)Scoring or ranking (e.g., FICO score)Optimization (e.g., risk management)

What is a Data Scientist?
Data Scientist responsibilities include extracting and analyzing huge amounts of data to identify trends and patterns, which may benefit individuals, businesses, and organizations. They use various analytics tools for advanced analytics and technologies that include predictive modeling and Machine Learning. Reporting and visualization tools are used to display the insights generated through data mining that will further enable one to make informed decisions that are customer-oriented and consider potential revenue opportunities, among many others.
The Data Scientist role is often a subset of many other traditional job profiles such as statistician, computer professional, mathematician, and scientist.

How to become a Data Scientist?

Data Scientist Qualifications
Data Scientists are expected to have a strong command over programming languages, such as Python, R, and SQL, and Machine Learning models and have workflow proficiency in Git and command line. Apart from this, these professionals also require strong communication, problem-solving, and data reporting skills.
It is not difficult to take up the role of a Data Scientist without prior experience in the domain. It is common for such aspiring individuals to transition from Data Analyst roles if they have no experience in the relevant field.
Where a Data Analyst will often explore answers for already available questions, a Data Scientist will need to explore the data in the first place to come up with relevant questions and potential business opportunities that have chances of being overlooked.

Data Scientist Educational Requirements
A Data Scientist will be expected to have a bachelor’s degree. Higher-level or advanced degrees may not be strictly mandatory to land a job (even with job descriptions that ask for such requirements). Most employers look for relevant skill sets in the field. Any applicant with less-relevant degrees can spruce up their portfolio with advanced skills and experience in relevant Data Science projects.
However, the educational requirements may typically include an advanced degree in computer science, mathematics, statistics, or Data Science. A number of certification opportunities are also available for Data Science aspirants, such as Certified Analytics Professional, Microsoft MCSE Data Management and Analytics, MCSA: Various SQL/Data Engineering options, and Dell EMC DECA-DS.
Take a look at the Data Science Courses offered by Intellipaat and enroll today.

Data Scientist Skills
The four fundamental skills required for Data Scientist are:
Mathematics (statistics and probability)Computer science (engineering, software architecture, and data architecture)Business or the domainCommunication (verbal and non-verbal)The above is not in any particular order of priority. People are usually strong in one or two of these four fundamental pillars.
Data Scientists are required to be familiar with a number of Big Data tools and platforms, viz. Hadoop, MapReduce, Apache Pig, Hive, Spark, etc., programming languages, viz. Python, Scala, SQL, Perl, etc., and the statistical computing language, R. Hard skills, including data mining, Machine Learning, Deep Learning, structured and unstructured data integration, etc., are essential in Data Science. Modeling, clustering, predictive analysis, segmentation, and data visualization are all statistical research techniques that play huge roles in this domain.

Data Scientist Job Description
As a professional Data Scientist, you will be expected to be skilled in:
All phases of the Data Science life cycleData Science, computer science, statistics, mathematics, economics, operations research, or other quantitative fieldsCommon data warehouse structuresWorking with a wide variety of data sources, databases, standard data formats, such as YAML, JSON, and XML, and public or private APIsStatistical approaches for analytical problemsCommon Machine Learning frameworksPublic cloud platforms and servicesQualitative and quantitative analyses and effectively sharing results with the audienceImplementing various Machine Learning techniques in business processes for improved efficiency and effectivenessDesigning and making use of reporting dashboards to provide actionable insightsVisualization tools such as Tableau and Power BIPython, R, or ScalaData aggregation from disparate sourcesMachine Learning techniques: K-nearest neighbors, support vector machines (SVM), Naive Bayes, decision trees, random forests, etc.Designing and implementing validation testsConducting ad-hoc analysis and presenting results effectivelyRequirements can vary from job to job. There are more and more specialized roles emerging in the industry. However, knowledge in the following key skills will be expected from any Data Science role:
Python or RSQLMachine Learning modelsProbability and statisticsData visualizationA Data Scientist has to have knowledge of the basics, but one role might require some more in-depth experience in one particular area, whereas another might be focused on different specifications.
Check out these Data Scientist Interview Questions and stay prepared for your next interview.

Data Scientist Roles And Responsibilities
Study the accuracy and effectiveness of data sources and data gathering methodsMine and analyze data to enable the optimization of product development, business strategies, and marketing techniquesBuild custom data models and algorithmsUse predictive modeling to optimize customer experiences, revenue generation, ad targeting, etc.Coordinate with different functional teamsCome up with the A/B testing framework and test model qualityDevelop tools and methods to monitor and analyze the performances of models and data accuracyIdentify opportunities with stakeholders in effectively using company data to drive business decisions and solutions
Data Scientist Career Path
As already mentioned above, many Data Scientists begin their careers first as Data Analysts, and then enter the field of Data Science via job changes or an internal promotion. Once experienced, these professionals can then look for senior roles in Data Science. More experienced Data Scientists who have management skills can go on to take up director-level and executive-level roles.
Here are a few ways one can stay active in the world of Data Science:
1. Follow Data Science groups and influencers
Joining Data Science groups is an effective way to stay updated and maintain relationships with fellow Data Science enthusiasts. Furthermore, attending meetups from time-to-time is also a great way to expand your network. Many Data Scientists and big players have turned to social media to share Data Science-related know-how. Make sure to follow these accounts for regular updates.
2. Build a public portfolio of Data Science projects
As a beginner, you can start building a simple portfolio of interesting Data Science projects you have taken up and showcase them on platforms such as GitHub. Not only will you be creating a personal brand from scratch, but it will also ensure future growth in your career path.
3. Go for an online training course and earn certification
The most effective and the least costly way to boost your skills is by enrolling in courses that develop hot skills such as Python, R, Tableau, SQL, and Machine Learning.
You can start with our video tutorial on Intellipaat’s Data Science course:

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4. Target employers
Every business, big or small, generates data. Not every company might be able to afford a full-sized team of Data Science experts. They need capable professionals who can do the work for them. Your job is to define your target employers and make a list of organizations that appeal to you. Once you start following them and stay updated over social media, you will get a clear picture of what you should be doing as a Data Scientist if you want to join their team.

A Data Scientist’s Tools
Let’s explore the quintessential tools found in a Data Scientist’s toolbox:
Programming Languages
RPythonSQLJavaScalaJuliaYou don’t necessarily have to be an expert in all of the above, but learning R or Python and SQL are strongly recommended.
Mathematics, Statistics, Modeling, Algorithms, and Data Visualization
Pre-existing libraries and packages are used wherever possible. Some popular Python-based ones are:
TensorFlowScikit-LearnNumPyMatplotlibPyTorchPandasBig Data Tools
HadoopKafkaSparkPigHiveDrillMahoutPrestoResearch and Reporting
JupyterJupyterLabRDBMS, NewSQL DBMS, and NoSQL
HadoopMongoDBMySQLRedisPostgreSQLRedshiftSnowflakeHBaseCloud-based Services and Cloud Computing
Microsoft AzureAmazon Web Services (AWS)Google Cloud Compute (GCP)DevOps and DataOps Orchestration and Deployment
Kubernetes (K8s)DockerIaC tools such as Terraform
Conclusion
Data Scientist jobs are in extremely high demand, and the domain can have a massive impact on any business in many different aspects depending on the business’s goals. This blog has tried to cover all the different paths that can lead you to a rewarding career in this domain.
Check out our Data Science Community and start a discussion.

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