CAREER PATH IN DATA SCIENCE

For the people who are wondering, how to start, where to start from, which stream to choose from, and other things, here’s a quick article to help you!

1. Basic difference and work description

A person who is unfamiliar with data may wonder what this “Data-Based Work” is about. The first thing that can come to mind is what the term data signifies. In general, data is any set of characters that is collected and translated, usually analyzed for some purpose. It can be any character, including text and numbers, photographs, etc. If data isn’t put into context, a human or a computer doesn’t get anything.

Data Analysts: In data science, a data analyst plays an important role. They perform several tasks related to the selection, organization, and obtaining from them statistical information. They are also responsible for displaying the data in the form of maps, graphs, and tables and using the same for constructing organizational relational databases. Data Analysts must be well versed in the art of data communication when it is required to communicate the findings of a data science project to certain parts. And, because data interpretation needs not to be full coding a data analyst may be a person who knows not too much coding!

Data Scientists: A Data Scientist is an expert who for the most part originates from an applied arithmetic or potentially insights foundation combined with software engineering. Data researchers will likewise have the foundation to pick proper AI calculations, train them, and devise strategies for testing their exactness. He/she comprehends information from a business perspective. He is accountable for making forecasts to assist organizations with settling on exact choices. What separates them is their splendor in business combined with extraordinary relational abilities, to manage both business and IT pioneers. Data scientists must be knowledgeable in the specialty of information narrating when the consequences of an information science venture should be passed on to business stack holders. This exertion requires the capacity to verbally and outwardly impart complex outcomes and perceptions such that the partner can comprehend and follow up on them. Data researchers ought to likewise have coding aptitudes on either the R or Python language. The programming abilities of an information researcher fundamentally shouldn’t be of that elevated level.

Data Engineers: A data engineer is someone who is from a background in programming. Their history is typically in such languages as Python, Java, or Scala. Our focus is on distributed and big data structures. Their programming skills are more advanced compared to data scientists, and primarily for building high-availability production systems, while data engineers often implement machine learning algorithms preferred by data scientists for a production environment. Data engineers’ programming skills are used to build on-scale data pipelines. This involves the integration of a variety of big data technologies. Data engineers must have a thorough understanding of application technology and the context

Machine Learning (ML) Engineer: ML engineers have a primary role to play in working with vast quantities of structured or unstructured data, and in developing and implementing machine learning algorithms. An ML Engineer should be able to design and create high-quality, manufacturing-ready code that can be used by cloud platform users within an enterprise. He/she should have extensive experience with a statistical language such as Python, R, etc. and knowledge of ML principles, should be able to manage a large number of data sets and distributed computing, should also have the definition with data mining techniques, etc.

1. SALARIES

Within data science, there are other work functions. Depending on the skills every work position is paid out differently.

• Data Analyst: INR 2 lakhs to INR 9 lakhs

• Software Developer or Programmer: INR 2.5 lakhs to INR 10 lakhs

• Data Analyst: INR 2 lakhs to INR 9 lakhs

• Senior Data Analyst: INR 3.1 lakhs to INR 10 lakhs

• Software Engineer: INR 2.5 lakhs to INR 10 lakhs

• Senior Business Analyst: INR 4.2 lakhs to INR 20 lakhs

Geographical Location

The data science salary also varies depending on the work location as you are paid the highest in Gurgaon, Chennai, and Bangalore, while the lowest in Delhi, Pune, and Hyderabad respectively.

Years of Experience

• The fresher can expect an annual package of INR 5 lakhs with less than 1 year’s experience.

• Professionals with 1–4 years of experience in their initial profession earn an annual package of INR 6 lakhs.

• Professionals 5–9 years of experience with INR 10 lakhs and INR 17 lakhs 10–19 years of experience respectively.

2. STEPS TO BECOMING A data scientist

a. Developing expertise in Algebra, Mathematics, and ML:

A data scientist is someone better than any software engineer in mathematics and better than any statistician in software engineering. The goal is to have the right balance, without an emphasis on one of the two is too much or not enough.

b. Learn to love (Big) Data:

Data scientists navigate a humorous amount of segregated and unsegregated data on which a single computer can sometimes not be used to perform computations. Many of them are using big data tools such as Hadoop, MapReduce, or Spark to get distributed computing. Many online courses will help you learn big data at your own pace.

c. Gain a Thorough Knowledge of Databases

Given the enormous amount of data produced virtually every minute, most industries use database management software such as MySQL or Cassandra for data storage and analysis. Clear insight into the DBMS’s workings is sure to go a long way in securing your dream career as a data scientist.

d. Learn to Code

You can’t be a good data scientist unless you know the language the data is interacting in. A well-categorized piece of data may be shouting its analysis; writing may be on the wall, but only if you know the script do you understand it. A good coder may not be a great data scientist, but a great data scientist is a good coder for sure.

e. Master Data Munging, Visualization, and Reporting

Data munging is the method of transforming the raw data source into an easy-to-study, interpret, and visual source. Data visualization and presentation are an equally valuable set of skills on which a data scientist relies heavily as strategic and administrative decisions are enabled through data analysis.

f. Work on Real Projects

After you’ve become a good data scientist it’s all about practice in theory. Look for data science projects on the internet and spend your time developing your power, along with zeroing in on areas that still need to be brushing up.

g. Look for Knowledge Everywhere

A data scientist is a team player and being a keen observer also helps when you are collaborating with a group of like-minded people. Learn how to cultivate the insight needed for data analysis and decision-making by closely observing your peers’ working habits and determining what suits you best.

h. Communication Skills

Communication abilities differentiate a big data scientist from a mediocre data scientist. Most often than not, you find yourself behind closed doors explaining to people who care about the results of your data review, and the ability to have your way with words will also be useful when coping with unexpected circumstances.

i. Compete

Websites like Kaggle are a perfect testing ground for budding data scientists as they seek to find partners and compete against each other to demonstrate their insightful strategies and fine-tune their abilities. With the growing prestige of the certifications offered by these sites in the industry, these competitions are fast becoming a stage to show companies how the mind functions innovatively.

j. Stay Up-to-Date With the Data Scientist Community

Follow blogs such as KDNuggets, Data Science 101, and DataTau to keep in touch with data science world events and gain insight into the types of job openings being offered in the sector at the moment.

3. Data Scientist DEGREES & CONCENTRATIONS

DATA SCIENCE CORE REQUIREMENTS

DATA SCIENCE THEORY CORES

• Algorithms for Data Science
• Advanced Algorithms

DATA SYSTEMS CORES

• Systems for Data Science
• Database Design and Implementation
• Distributed and Operating Systems

DATA SCIENCE AI CORES

• Natural Language Processing
• Machine Learning
• Data Visualization and Exploration
• Machine learning: pattern
• Visual Analytics

While there are many skills required in data science due to its multidisciplinary nature, the three basic skills that could be considered as prerequisites for data science are mathematics skills, programming skills, and problem-solving skills.

A degree in an analytical discipline will give you the basic skills needed in data science. Anyone with a solid background in an analytical discipline might essentially learn data science through self-study.

If you have a background in an analytical discipline and are considering data science, here are some tools you can use to research for yourself:

(i) Professional Certificate in Data Science (HarvardX, through edX)

(ii) Analytics: Essential Tools and Methods (Georgia TechX, through edX)

(iii) Applied Data Science with Python Specialization (the University of Michigan, through Coursera)

(iV) “Using the Python System,” by Sebastian Raschka. This book provides a perfect introduction to data science and machine learning by Sebastian Raschka, with code included: “Python Machine Learning.” Through machine learning, the author describes basic principles in a way that is very easy to understand. The code is also included so that you can use the code given to practice your models and create them.

Let’s now discuss 5 best degree programs that can easily lead to data science.

Best Degrees for Getting into Data Science

1. Physics

I just want to put physics at the top of my list. I might be biased here because by training I am a physicist myself. Yet I think this is well justified in this list. A degree in physics is one of the most flexible grade programs out there. A degree in physics offers solid foundations for problem-solving, analytical skills, maths, and programming skills. These are easily transferable skills. That explains why you can find physics graduate holders working in various fields such as education, technology, banking and finance, research and development, software engineering, law, military, data analyst, etc.

2. Mathematics

I’d put maths in my list as the second. Mathematics is also a very flexible profession, just like physics, and a background in mathematics can lead to multiple disciplines such as banking and finance, engineering, health sector, research, and development, etc. The most important competency in data science is a strong background in mathematics and statistics.

If you are pursuing a degree in mathematics and are considering data science, make sure you take some classes in programming. Both in simple and advanced statistics and the probability, it is important to take some classes too.

3. Computer Science

A degree in Computer Science is third in my ranking. Computer science educational programs, including physics and mathematics, offer an excellent foundation in problem-solving, geometry, and programming skills. In data science the programming skills are key.

If you are actually in a computer science degree program and are considering data science, make sure that you are taking some math classes such as calculus, linear algebra, statistics and probability, and methods of optimization.

4. Engineering

Any degree program such as mechanical engineering, electrical engineering, or industrial engineering will provide you with the analytical skills that are necessary for data science.

If you are currently in an engineering degree and are considering data science, make sure that you are taking some programming classes and some basic and advanced statistics and probability courses.

5. Economics, Accounting, or Business Degree

A degree may also act as a gateway to data science in one of these areas. Compared to programs such as physics and mathematics, the analytical skills offered in these programs that lack the quantitative rigor, but a degree in economics or accounting would provide one with business skills that are necessary for real-world data science application.

If you are currently in economics, accounting, or business degree program and are considering data science, make sure that you are taking some math classes such as calculus, linear algebra, statistics, and probability, as well as programming classes as well.

4. COMPONENTS OF A SUCCESSFUL Data Scientist CAREER: SKILLS, TOOLS, AND TECHNOLOGY

Python Programming:

Some basic python programming is the very first thing you should be doing. To get started, learn the types of syntax, variables, and data, lists and for loops, conditional statements, dictionaries, and frequency tables, functions, and object-oriented python.

Data Analysis and Visualisation:

Now we want to know to analyze and visualizing data. First, you’ll want to start by learning pandas and NumPy to clean up your data and explore it. You’ll then want to use matplotlib with your data for exploratory data visualization and storytelling.

Command-line tools:

Next, you want to know how to navigate the directory of data, how to build and delete folders, how to modify and manage data and their permissions, how to interact with command-line programs and how to construct the virtual world. For version control, you’ll want to know about git and GitHub too.

Databases:

You’ll want to know SQL to access data and PostgreSQL for advanced database management. Also, you should be able to work with APIs and web scraping to build your datasets. Seek also to know spark and to rising maps

Statistics

Next, you’ll want to know simple statistics that include sampling, distribution of frequencies, mean, weighted mean, median, style, variability measurements, Z-scores, likelihood, distribution of chance, significance testing, and chi-squared testing.

Machine Learning

You’ll want to know at least ten simple machine learning algorithms: linear regression, logistic regression, SVM, random forests, gradient boosting, PCA, k-means, collaborative filtering, k-NN, and ARIMA.

You will also need to learn how to test model efficiency, hyperparameter optimization, cross-validation, linear and nonlinear functions, basic calculation and linear algebra, selection and preparation features, gradient descent, binary classification, overfitting and underfitting, decision trees, neural networks, and then create something with those skills and even try some kaggle co You may also move on to more advanced subjects such as the NLP and AI.

5. CAREER TRENDS IN Data Scientist

If online job listings for data science are anything to go by, the demand for this position has maintained a steady spiraling upward in the last few years. This supports the Internet soothsayers’ predictions that they are thinking about substantial growth in data science in the coming years. Indeed, one study reports that the size of the industry has risen to $3.03 billion, and is projected to double by 2025.

Leading the fray is Quora’s Head of Data Science Eric Mayefsky who additionally feels that certain developments will accelerate this development, which will have a particular effect on the data science career fundamentals.

Such shifts, organized around many patterns, will give you a clearer understanding of what to expect in the future and what you should do to take advantage of the opportunities ahead:-

Diversification In Titles

This may be the biggest shift you can see in the coming years inside data science. Even as more businesses rely on data-driven teams, and thus search for more data science roles to fill, the reality is that most of them have little understanding of how divergent the task could be. Exposed here, this leads to inefficient recruiting practices, disappointed employers or disillusioned workers, and often both.

That was not the case for technical positions, however. Most organizations can easily differentiate between a mechanical engineer and a software engineer, and even at the initial stages of the entire hiring cycle, this proves valid.

While widespread acceptance of ‘data scientists’ has helped bring this field to prominence, there needs to be a greater understanding of how to diversify this large area. It is no longer enough to simply emphasize the importance of recruiting people to think about your product or service from a data perspective.

However, with this position spreading wider and deeper through industries, branching out into well-defined weapons is probable. This will also mean expanding the scope of data science and thus emerging some specializations.

More ‘Non-Tech’ Companies

A phenomenon that has arisen in recent times is that businesses that historically described themselves as ‘non-tech’ are starting to position themselves as tech firms and this is likely to continue.

Banks are a case in examples. For example, as long as they attempt to monetize the data assets of the business, the word ‘analyst’ used in the sense of this sector may now be called a ‘data scientist.’

The copious quantities of data available today are one of the key reasons for that trend — and this has been growing exponentially. What’s more, fueled by the emergence of IoT and social media (Internet of Things), this development is not expected to slow anytime soon.

Reportedly, the IoT market alone in India will cross a whopping 2 billion connections by 2022. It is underlined by the fact that the form of data generated would be more varied not only with more devices coming online but with greater hardware upgrades.

The same is true of social media. The number of social media users worldwide increased to 3,484 billion in 2019, Hootsuite said, showing a rise of 9 percent y-o-y. In addition to conventional social media networks, blog proliferation, electronic payment transactions, surveillance data, etc. are now contributing to this large data collection.

Through digitization, these data sources will continue to expand, encouraging companies to collect even greater quantities of consumer information to drive their business strategies.

Besides, the effects of this trend of rising data would shore up to other fields, boosting data science demand in ‘non-tech’ firms.

Continue To Create Value

In your career, the field of data science will continue to bring a lot of value — even if you just start. And this will continue to be true because that function will be generated by the growing demand for data. It is also true across positions-from a data analyst to a system engineer.

In data science jobs, there is no defined hierarchy, and wages vary widely even within a single job, depending on how overarching it is. That ensures you have a relatively open-ended career and you can travel around and take up jobs according to your preferences and skills.

6. JOB OUTLOOK FOR DATA SCIENTIST

As the U.S. of Labor Statistics has reported, the employment of all computer and information technology scientists is projected to increase 19 percent by 2026, which is considered much higher than the rate for all occupations. Over the decade approximately 5,400 new jobs are expected. As the demand for new and advanced technologies grows in the field of data science, there will be a growing demand for skilled data scientists. The rapid increase in data collection would lead to an increased need for data-mining services.

7. HOW TO PREPARE YOUR PROFILE FOR THE JOB

BUILDING RESUME

• Should be easy to find relevant information in 6 seconds or less
• Highlights only the best/most important experiences
• Visually stands out against the sea of cookie-cutter applications
• Use the correct formula to frame your projects and experiences in terms of business impact(even if they were personal/academic projects)
• Format: What you did -> How you did it -> Impact it made
• Bad: built recommender system in python
• Good: built recommender system in python using collaborative filtering and matrix factorizations that resulted in a 3% increase in basket size and a $3M increase in yearly revenue
• Make sure your resume is easy to read — use www.readable.io and aim for a 5th-grade reading level
• Make sure you have the proper keywords that using www.jobscan.co

Your LinkedIn:

• Translate your experiences from your resume to your LinkedIn
• Create a summary that shows your unique skills and personality
• Take a professional profile pic that is friendly and makes you more trustworthy
• Fill out the skills sections with the right skills so that recruiters find you(cut the extras that clutter your profile)
• Begin applying for jobs through LinkedIn
• Send follow up messages — (find 3–5 key decision-makers (these will most likely be people in HR for the company you applied for) and send them to follow up messages)
• Quickly and simply show your enthusiasm for their company
• Briefly pitch your unique skills and how they’ll help the company(just give a preview of what you can do)
• Keep the follow-up messages to 5 sentences max. (shorter is better and more likely to be read)

Your Portfolio:

• Your projects should tell an easy to follow a story
• Should visualize your results
• Should be well-documented with high-quality, organized code
• Includes a clear write of what you did and why
• Demonstrates you can do the job of a data scientist

8. COMPANIES HIRING

Indian Based companies which are hiring are:

· SocialCops, Delhi: you can get in touch with Prukalpa Sankar to know more. They work mostly on classification and predictive analytics problems

· FreeCharge: Behaviour prediction and Recommendation engine

· Snapdeal: Recommendation engine, delivery route optimization

· Myntra: Recommendation engine, warehouse workload distribution, demand distribution prediction

· Flipkart: Recommendation engine, fraud detection, predictive analytics

· VMWare: Sales prediction

· Thoughtworks: Prefer Masters and Ph.D. candidates

· Bloomreach: Recommendation engine, ad network

· Mu Sigma: is always hiring, it seems.

· Helpchat: Chat assistant

· Haptik: Chatbots

· Belong, Bangalore: Search, predictive modeling and Recommendation engine

· Samsung Research Institute — Bangalore: Machine Learning — Classification and Scoring problems, Signal Processing, Lot of NLP and predictive analytics

· Microsoft Research, Bangalore: Very research-driven and focussed on great publications, works on bleeding edge ML and other problems

· ParallelDots, NCR — AI API stack

· Bigbasket

· Ola

· Mad Street Den, Chennai — excellent product on Image processing and Recommendation systems

The companies which are located abroad are:

Accenture: Accenture is a consulting company for regional strategy and technical services. It offers a variety of policy, digital services, infrastructure and activities, and currently partners with over three-quarters of the Fortune Global 500.

The company employs data scientists, data analysts, data developers and AI software engineers at its site in Dublin

Fidelity Investments: The company is currently searching for its Dublin office for an investment data analytics analyst.

Bank of America Merrill Lynch: Bank of America Merrill Lynch is one of the world’s leading financial companies offering expertise in mergers and acquisitions, equity and debt capital markets, lending, trading, risk management, analysis, and the management of liquidity and payments.

For its US locations in Charlotte (North Carolina) and Atlanta (Georgia), the investment banking division employs data scientists.

Aon: It is looking for a data engineer for their office in Dublin.

Bristol Myers-Squibb: BMS hopes to hire a data analytics scientist from its large-scale biologics factory in Devens, Massachusetts.

Oath: It is looking for a data center engineer to work in its base in Singapore and a data scientist for its center in Champaign, Illinois.

MSD: MSD is currently hiring for a plant maintenance data lead in Cork.

Intel: The tech giant currently has data scientist roles available at its various offices in California, Oregon, Arizona, Texas, and Massachusetts.

Pramerica: The company has a range of data science positions up for grabs.