Career Path for Data Science — How to be that Data Scientist?

Published in

JanBask Training

12 min readSep 16, 2020

The demand for Data Scientists who could extract, pre-process, and analyze data is shooting through the roof. Businesses from almost all major industry verticals, be it software, healthcare, banking, online retail, or more are on the constant lookout for Data Science experts to garner them actionable insights from big data.

With:

2.5 quintillion bytes of data produced every day.
Data Scientist’ as the sexiest job title of the 21st century.
40 zettabytes of data to be produced by 2020 and beyond.
Data Scientist salaries for job postings in the US are 80% higher than average salaries for all job postings nationwide.
The U.S leading the Data Science market.

It is made evident that now is the best time to explore the Data Science career path. And to help aspirants and job seekers explore a career path in Data Science, here in this guide we will inform and educate aspirants how they can shape their career trajectory in the 4 biggest Data Science roles — — keep rolling to find that out!

Let’s give you a complete hawk-eye expanse of what kind of tools, skills, and capabilities from where you should equip before entering the data analysis workforce.

Before that — Quick sneak-peek into — — Who is a Data Scientist?

It is not untrue to say that a Data Scientist is an analyst, data visualizer, and business intelligence expert — they are pretty much made of different work profiles (about which you know later on).

In a general context,

“Data Scientist is a transformation expert cum problem-solver who is part mathematician, part Computer Scientist, and part business trend spotter who bestrides both the IT and the business worlds.”

Data Scientist collects, analyzes, and interprets large volumes of data to improve a company’s operations.
Develops statistical models to analyze data and detect trends, patterns, and relationships in data sets. To help businesses predict consumer behavior & analyze business and operational risks.

Data Science is integrated with industries across all work IT to non-IT sectors. That’s why Data Scientists are expected to acknowledge a broader set of skills with more cohesive specialization and collaboration.

Skills Required for Data Scientists

To break into a career path for Data Science, aspirants are required to have hard skills like data analysis, machine learning, statistics, data visualization, Hadoop, etc. Along with soft skills like critical thinking, problem-solving, persuasion, communication, and a great listener.

Top 10 skills Data Scientists are expected to have ingrained:

To make Data Science as a career path, you need to hone the following few skills!

Skill

Scope of that Skill

Probability & Statistics

To explore and understand more about the data.
Identify the underlying relationships or dependencies that may exist between two data variables.
Predict future trends based on previous data trends.
Determine the motive/patterns of the data.
Uncover anomalies in data.

Multivariate Calculus & Linear Algebra

Data Science models are built with several predictors or unknown variables. Knowledge of multivariate calculus helps with building a machine learning model.

Programming, Packages, and Softwares

Having programming Skills for Data Science brings together all the fundamental skills required to transform raw data into actionable insights.
There is no specific rule about the selection of programming languages, but Python and R are the most favored languages trusted for data analysis.

Database Management

Database Management consists of a group of programs that edit and manipulate the databases.
The database management systems (DBMS) are made for data from an application and guide the operating systems to provide specific required data.
In large systems, a DBMS helps users in storing and retrieving data at any given point of time.

Data Wrangling

Data Wrangling is the process to prepare given data for further analysis — — transforming and mapping raw data from one form to another to prepare the data for insights.
In data wrangling, you acquire data, combine relevant fields, and then cleanse it for more clarity processing.

Data Visualization

Data Visualization is the graphical representation of the findings from the data under consideration.
Histograms, Bar charts, Pie charts, Line plots, Time series, Relationship maps, Heat maps, Geo Maps, 3-D Plots, and more are means to visualizing the data.

Machine Learning / Deep Learning

Machine Learning or ML is a subset of the Data Science ecosystem, similar to statistics or probability it contributes to the modeling of data & getting the results.

Microsoft Excel

Best editor for 2D data.
A platform for advanced data analytics.
Gives live connection to a running Excel sheet in Python.
Data manipulation is quite easy.
You can do whatever, whenever you want, and save as many versions as you prefer.

Cloud Computing

For data acquisition from the cloud.
For data mining [Exploratory Data Analysis (EDA), summary statistics.
For parsing, munging, wrangling, transforming, analyzing, and sanitizing data.
For validating and testing predictive models, recommender systems, and more such models.
For tuning the data variables and optimizing model performance.

DevOps

To provision, configure, scale, and manage data clusters.
To manage information infrastructure by continuous data integration, deployment, and monitoring.
To create scripts for automating the provisioning and configuration of the foundation for diverse environments.

Data Science Career Prospects — Diverse Biggest Roles

With more and more companies spanning across all the sectors adopting Data Science and Analysis, they are hiring Data Scientists by hordes.

In the Analytics ecosystem, 70% of the job postings are for data scientists with less than 5 years of work experience.

Though the demand for Data Science is intense, despite that companies are inefficient at meeting the shortfall in talent. It is either because the aspirants aren’t aware of the benefits of approaching a future-proof data Science career path or they don’t know how to.

In the latter half of the post, you will know the reasons why you should make Data Science as a career path and how.

To bring clarity, here are the “big four” Data Science profiles that you can get into.

1. Data Scientist

Data scientists translate a business case into great analytics by understanding data and exploring patterns to gauge what impact they can/will have on business products or activities.

They use business analytics to explain what aftermath the data is going to have on a company in the future and even helps in planning & implementing solutions that will help the company in restoring its stability and growth.

Role of Data Scientist

To unlock the insights of data and tell a complete story or trend via the data.
Data Scientists in modern workplaces build machine learning models for prediction of datasets, find possible patterns & trends in data, visualize data for better understanding, and pitch in for improvement strategies.

Required Skillset: They should know about/have Statistics, Data Modelling, Mathematics, Python or R programming, Database skills, Visualisation/BI, Presentation Skills, and Business acumen.

2. Data Analyst

Data Analysts collect, process, and perform statistical analyses on given large datasets from various sources to help businesses improve products & decisions. They identify how data can be used to solve the business’s problem statements.

Role of Data Analyst:

To gather the company’s data & generate actionable insights upon which the C-suite can take action.
The interesting thing about Data Analysts’ jobs is that their projects usually change from time to time. For 3 months, they will be required to work with the marketing department, for next maybe production, and so on.

Required Skillset — Knowledge of Data Modelling, Python & R programming, Tableau, Business acumen, Database Cleansing skills, Visualisation/BI, Presentation skills.

3. Data Engineer

Data Engineers are the data people who prepare the “big data” infrastructure for analysis by Data Scientists. They are engineers who design, build, and integrate data from various resources and manage big data.

Role of Data Engineer

Since Data Engineers work with the company’s core data infrastructure, they are required to have deep knowledge of programming skills.
In most organizations, a data engineer is required to build data pipelines and correct the data flow to ensure that data is reaching the relevant departments.

Skills required: Database management, data cleaning, Python & R programming, Hadoop for data processing, Business acumen, Visualisation/BI, Presentation skills, Database cleaning skills.

4. Business Intelligence Developer

BI or business intelligence developer is an engineer that develops & maintains BI interfaces. With query tools, ad hoc reporting, data visualization & interactive dashboards, & data modeling tools.

Role of Business Intelligence Developer

A BI developer role requires researching and planning solutions for existing problems within the company.
They are also responsible for building OLAP (online analytical processing).
They also work with databases, both relational and multidimensional.

Required Skillsets: Python & R programming, Hadoop, creating models, Github, Notebook, data model, Business acumen, Visualisation/BI.

Latest Data Scientists Salary Trends

For 2020, Glassdoor has titled Data Scientist as the 3rd most desired job in the US with more than 6500 openings and an average annual salary of $107,801 along with a job satisfaction rate of 4.0.

Data Science Salary in the United States

As per Indeed, the annual average Data Science salary is $123,785 and $113,436 as per Glassdoor.
Entry-level professionals can earn between $50,000 to $90,000 (depending on your prior background, education, location, etc).

Data Scientists Salary in Diverse Locations

Wondering where a Data Scientist can relocate for a new job? Here are the few places that provide the highest data analytics salary on offer.

Industries Where Data Scientists are Hired

Banking, consulting, software are the biggest industries with demand for Data Scientists as they have voluminous data to be interpreted.

Demand for Data Scientists by Top Brands

Regardless of the current state of most industries, data will keep on generating, and there will always be a need for someone to make sense out of it. The current situation might have put a damper on a lot of companies’ hiring plans, but out in the void, there are still quite a few top companies hiring Data Scientists incessantly. Let’s know them!

Adobe — A computer software MNC, creating multimedia & creativity software products, recent foray towards digital marketing software.

Data Science Consultant — $65,000-$115,000

2. Amazon — A composition of e-commerce, cloud computing, digital streaming, and artificial intelligence solutions.

Data Scientist — EU Workforce Staffing — $83,000-$128,000

3. Aetna — Leading diversified healthcare benefits companies, serves approx 46.7 million people with information & readable resources to help make better lifestyle & health-based decisions.

Lead Data Scientist-Member Analytics — $150,000-$235,000
Principal Data Scientist-Clinical Products -$167,000 — $180,000

4. Apple — Designer, developer, and seller of consumer electronics, computer software, and online services patented at Mac.

Data Scientist, Power and Performance- $89,000 — $144,000

5. Bloomberg — Global IT company, that uses its dynamic network of data, ideas, and analysis to solve everyday complex problems.

Senior Data Scientist — Enterprise Risk Management — $49,000-$107,000

6. CVS Health- An American healthcare company owning a CVS Pharmacy, a retail pharmacy chain, a pharmacy benefits manager, Aetna, a health insurance provider, out of many brands.

Senior Data Analyst — $71,000-$99,000
Analytics, Lead Data Engineer — $67,000-$115,000

7. Bose — A manufacturing company of audio equipment like speakers, headphones, and headsets.

Business Data Analyst — $75,000-$108,000
Senior AI, Machine Learning and Data Science Leader — $91,000-$147,000

8. Procter & Gamble — An American multinational corporation for consumer goods.

Data Scientist — $97,000-$154,000
Data Scientist- Supply Chain Analytics — $100,000-$123,000

9. Spotify — Popular music streaming service with a community of 217m active users, across 79 markets.

Data Scientist — Metrics — $107,000-$174,000
Data Science Practice Lead- $119,000-$193,000

10. Uber — Popular ride-hailing company offering ride service hailing, ride-sharing, food delivery, and a micro-mobility system to commuters.

Sr Manager, Data Science- Maps and Uber AI — $77,000-$157,000
Sr Data Scientist — Transit — $157,000-$195,000

How to Pave a Career Path for Data Science?

With Data Science and Data Analytics becoming a crucial part of most organizations, the discipline has attracted wide attention amongst IT professionals & engineering graduates.

The application of Data Science, Data Analytics, big data, programming, and coding has added to the curiosity & learning interests of students from the STEM (Science, technology, engineering, and mathematics) background, who want to upskill around big data and its business applications.

Even 71.6% of the aspirants have said that they want to study Data Science via an online training program. Want to pave a career path in Data Science & Analysis? Here are the 3 steps to go about it.

Data Science Career Path Step 1 — Data Science Certification Training

To get familiarity with the industry demanded data analysis skills, it is best to join an online Data Science Training program that has real data scientists to teach, real-projects to establish skills for real fields, smart dashboard with diverse study materials to keep learnings interesting and purposeful, and Data Science certification & jobs assistance.

Why should I join the Data Science Training online?

Experts around to take on doubts
Competent syllabus & study resources
Exclusive preparation for Data Science Certification exams
No prerequisite to join the training
Free demo & counseling to test what you’ll get
Interview & job application support
And much more…

Data Science Career Path Step 2 — Qualify the Data Science Certifications

Seeking the industry demanded certifications will help in validating your skills. However, certifications are optional and shouldn’t be the milestones. But if you plan to approach it along with a rigid skillset, you will get the preference in the hiring rounds and will even have weightage in demanding the desired salary.

The Data Science Certifications you can approach after ending the training:

Apache Hadoop certifications
Certified Health Data Analyst
Data Science EMC Proven Professional
IBM Cognos Business Intelligence certifications
Microsoft Certified Solutions Associate
Microsoft Certified Solutions Expert
Oracle Database 11g Administrator Certified Associate
Oracle Database 11g Administrator Certified Professional
SAS Certified BI Content Developer
SAS Certified Predictive Modeller
SAS Certified Statistical Business Analyst

Career Path for Data Science Step 3 — Take industry projects/internships

After completing the training and acing the certification, it is best to work on the industry’s part-time projects or internship programs to gain a taste of real-work. This will add a lot to your portfolio and will help in eradicating the fresher badge.

When you are done with the projects, internships, create a stellar portfolio, and start reaching out to real full-time Data Science profiles.

So this was the complete guide about a career path in Data Science — — understand it and proceed accordingly!

Final Thoughts on Complete Data Science Career Path!

Paving a career path in Data Science is a great deal if you are looking for a job that pays heavy, constantly evolves, has immense scope for growth, and allows climbing up across the ladder.

Data Scientists are paid heavily as there is a shortfall in talent supply. Companies are extremely open to offering desired salary packages now to individuals who show up with the right skills & certifications.

And to get the right skills and certifications, self-taught learning journeys won’t be sufficient, you would be needing the upper hand from professional training online — — to get one-hood preparation right from syllabus preparation to job interview preparation.

While if you are unsure of opting for Data Science as a career path, there is the best way to find about that — — a free demo class & career counseling!