The Definitive Guide to Transition Your Career From Software Developer to Data Scientist in 2019

The world generate over 2.5 quintillion bytes of data every day on the internet, inside our business organizations and while using various electronics and digital devices like phones, computers, watches, cars, machinery, and various other gadgets.To process and present this data in a way that can help business decision making we need data scientist. Becoming a data scientist is amongst the most lucrative career option for the modern world.
As you know it, everyone is busy constantly producing videos, audio, text, graphics, presentation and various other content to interact, engage and convert people into paying customers — companies across Digital, FMCG, Finance, Manufacturing, Retail, Technologies are looking at these data to turn it into information and insights. The indomitable success of new economy companies like Google, Facebook, Tesla, LinkedIn, etc. are largely dependent on how they can convert the accumulated data to drive actions to guide people.
The unprecedented accumulation of data coupled with the high demand for data analysis needs globally by leading businesses, governments and innovators have fuelled the exponential demand for skilled data science professionals. Data scientist job postings as a share of all postings on Indeed jumped a full 31% in December 2018, compared with the same period in December 2017. Most major job portals have seen a similar rise in the job postings for data scientist position. However, the availability of skilled professionals doesn’t match the demand.
The purpose of this guide is to help you with actionable insights which you can use for deciding to transit yourself as a data scientist or in data science field opportunities. This guide will help you in deciding that if you are a software developer aiming to pursue more lucrative career opportunities, to help you earn a better salary, get into a more meaningful career and future-proof your career.

How Companies are using Data Science?

You make up to this point simply means that you have a deep interest in exploring the career options and roles available for data scientists.

Business Intelligence (BI) Developer

In a constantly changing economic landscape, businesses are in search of finding the emerging trends, competitive landscape and opportunities that they can tap to survive and thrive. You can present yourself as a Business Intelligence Developer provided you have the interests, skills and personality for the following:
Expert in gathering requirements & translating business requirements into technical solutions.
You know the techniques required for data visualization design, development and integration.
Capabilities to implement BI projects from start to finish.
You’ve developed expertise for using DAX query for building complex formulas/measures such as creating dynamic chart headers as per the filter selection, you have got SQL skills for writing custom queries to aggregate and extract data, database design and architecture.
As a BI developer, you will be required to present the outcome to the business users. You must have good written and oral communication skills to build a relationship with the stakeholders.
Ability to understand the business context and apply analytical concepts to provide business solutions.

Data Architect

As a Data Architect you will be involved in providing an idea data management solution for the projects. You will be expected to be functionally knowledgeable in multiple Big Data and NoSQL Technology areas and hands-on in data management and programming. You will provide solution architecture for the business problem, platform integration with third party services, designing and developing complex features for business needs.
You will also be expected to show your capabilities for leadership, mentorship, systems analysis, architecture, design, configuration, testing, debugging, and documentation.
Own and drive the evaluation, adoption, design and architecture Big Data technology.
Work with Product Owner/Business Analysts to understand functional requirements and interact with other cross-functional teams to architect, design, develop, test, and release features.
Develop Proof-of-Concept projects to validate new architectures and solutions.
Drive common vision, practices and capabilities across teams.
Engage with business stakeholders to understand required capabilities, integrating business knowledge with technical solutions.
Engage with Technical Architects and technical staff to determine the most appropriate technical strategy and designs to meet business needs.

Applications Architect

In the application architect role, you will work on a variety of projects and will play a key part in making sure that end results are designed according to the planned goals, relevant patterns and analyses.
You will be required to design and implement solutions getting involved in setting up the Logical and Physical Architectures.
Ensure that the project and solution assumptions made during project planning and scoping are validated.
Comprehend business strategies and requirements and develop necessary designs and plans to ensure projects and solutions satisfy those needs.
Participate and own the Functional Requirements Document and Specification, directly contribute to the Technical Requirements Document.
Act as a contributing member of the project team from project inception to completion.
Ensure functionality is consistent with project requirements.
Collaborate with project managers and other staff members to develop budgets and timelines for solutions.
Maintain balance between requirements and efficient solutions.
Track the behavior of applications used within a business and how they interact with each other and with users.

Infrastructure Architect

Oversee that all business systems including the data center, software and applications are working at acceptable performance level and can support the development of new technologies and system requirements.
You will be responsible for design, development, implementation, operation improvement and debug for public and private Cloud Management.

Data Scientist

To attract the employer to hire you as a Data Scientist and continue to remain relevant for the job, you will need to have the following key skills:
Complete knowledge and experience in data science and use of statistical methodologies.
Experience capturing, assessing and making recommendations, including reviewing data for completeness and consistency, analyzing and interpreting data.
Developing and analyzing data using machine learning methods and techniques such as clustering, regression, optimization, recommendation, neural networks, and others.
Strong quantitative and analytical skills with experience with data science tools using Python, R, Scala, Julia, or SAS.
Understanding of data science disciplines such as mathematics, statistics, computer science, physics, and other related fields.
Familiarity with using cloud services and Big Data tools to develop data science solutions.

Data Analyst / Data Engineer

As a Data Analyst, your employer will desire that you have good business and product knowledge; especially in the area of Data management, design, architecture and dashboard development.
You are expected to possess good command over the written and verbal communications skills and presentation skills.
You must be aware of the best practices for industry data management, tools, and processes for data management, data warehouse and report development.
Must have good programming skills in Python, Java, R or other statistical programming tools.
Machine Learning Specialist / Engineer
Since Machine Learning is the core of Data Science, the specialists play a vital role in leading and contributing to define an overarching big-data-driven approach to advanced analytics strategy and architecture.
You will influence the strategic direction by identifying opportunities in large, rich data sets and creating and implementing data-driven strategies that deliver the results. Create visualizations to connect disparate data, find patterns and tell engaging stories.
Working with business domain, IT and data experts to identify detailed data needs, sources, and structure to support solution development and deployment.
Develop methods for preparing analytical datasets for model development, documentation, implementation, and validation.
Deploy statistical/machine learning models into a platform or application.
Statistician
If you have got the flare for math and statistics, you can use it to help businesses analyze business challenges to be solved, develop analytical models, evaluate alternatives and identify solutions. As a statistician working for the Data Science team you will be expected to deliver the following:
Collaborate with team members to lead research studies and ensure that the appropriate statistical models are designed and developed for the given project.
Contribute for optimizing the design, analysis, interpretation of results and conclusions for research related studies and trials.
Deploys a range of scientific, mathematical, computational and/or data analysis methods for developing technologies.
Documents and maintains a process for developing statistical analysis plans and applying the appropriate models as per the study design.

What Do Data Scientists Do and What Skills Do You Need to Become a Data Scientist?

If you have gone through the above job role specifications for the various positions that businesses hire data science professionals, you will find that their primary responsibility is to extract meaning from the data. They require skills and experiences to present insights to help steer strategic business decisions which require both tools and methods from statistics and machine learning, as well as use human intelligence. You as a data science professional will spend a substantial amount of time in the process of collecting, cleaning, and processing data. To achieve your goal of converting data into meaningful insights you will be expected to apply persistence, statistics, and software engineering skills, and debugging techniques.

Mathematics Expertise

You will require the ability to view the data through a quantitative lens. Using the mathematical models and statistical methods, you can establish the correlation between various data sets and express it mathematically. Having a strong mathematical and statistical foundation for visualizing the solutions to many business problems. You’d be at an advantageous position if you know these mathematical and statistical methods:
Logarithm, exponential, polynomial functions, rational numbers.
Basic geometry and theorems, trigonometric identities.
Real and complex numbers and basic properties.
Series, sums, and inequalities.
Graphing and plotting, Cartesian and polar coordinate systems, and conic sections.

Software Engineering Skills

As a data scientist, you will be dealing with the programming skills to gather, extract, clean and present data in a meaningful way. A good programming skill will help you deliver better result:
Your ability to develop software in a way that it can be used by others, including documentation, installing packages, configuration management, debugging and running computational studies.
Creating technical specifications. Creating, updating, and sharing a project using version control tools.
Programming in Python using the Python scientific stack, including libraries, APIs and other tools.
Developing unit tests and using test-driven development to build software.
You can learn more about how to become a better software developer here.

Business Problem Solving

You will be dealing with complex business challenges being faced by the business today. You’ll be working together with the business and operation management team to understand data, pattern, and insights that they are looking for to make the decision. Your ability to quickly understand the business environment and translate observations to shared knowledge, and contribute to strategy on how to solve core business problems will determine largely the success that business derives from the data science. Your usefulness will be proven by leveraging the tech, algorithms, data management and analytics capabilities to build insights that deliver the strong business value proposition.

Machine Learning Skills

Having proficiency in machine learning techniques will help you solve different data science problems which are dependent on predictions of outcomes.
You’ll find yourself at an advantage position if you possess machine learning skills like neural networks, reinforcement learning, adversarial learning, supervised machine learning, decision trees, logistic regression, unsupervised machine learning, Time series, Natural language processing, Outlier detection, Computer vision, Recommendation engines, Survival analysis, Reinforcement learning, and Adversarial learning.

Top Job boards for Data Scientist Job

Ai.jobs.net ai-jobs.net aims to provide the most comprehensive job board related to all things AI, ML and Big Data Worldwide. The focus lies on a simple mobile friendly site with no fluff and simple but effective job ads. It’s simple to create job alert as the search allows for filtering by region so job seekers can add regional jobs to the result list.

Amazon Jobs
Amazon is a leading employer when it comes to data science jobs with a broad spectrum of job roles from data analyst to data scientist to WWPS data Analyst Intern.

The company states that Data Scientists are the links between enterprises and the technical side of Amazon. There are more than 200+ Data science related job opening at Amazon at present.

Big Data Jobs
Created by Software engineers with over a decade of industry experience to make it easier for employers to identify and attract top big data talent. It’s simple to find a job like you can filter jobs based on location, keywords, and distance. They have vast follower base on Twitter where they tweet regularly about current job vacancies and also send weekly email newsletter to keep you updated.

Data Elixir
Data Elixir is an email newsletter that keeps you on the top of the tools and trends in Data Science. All the data science jobs featured on the website are also posted in Data Elixir’s email newsletter and shared on social media. It has grown into one of the largest data science job boards in the country. It’s simple to search for jobs by keyword or location. What makes it more popular is you can also have future vacancies that match your selected criteria, emailed to you as soon as they are posted.

Datajobs
Job board for everything big data – data scientists, DBAs , Hadoop technology, analytics, data analysts. Its focuses on two type of categories

Data science: You can find jobs like advanced analytics manager, lead data scientist, performance analyst etc.

Data Technology: Its dedicated to roles like data engineers, data quality engineer and senior information architects.

You can also search for jobs based on the title, keyword, the company, city, state, or zip code.

Glassdoor
It’s one of the world’s largest recruitment websites. It’s a great platform to find big data jobs. One of the best things about glassdoor portal is its providing detailed company reviews, salary reports, interview reviews and questions, CEO approval ratings, benefits review and even office photographs and all this data shared by current and former employees.

Kaggle
Kaggle is one of the world’s largest online community of data scientists and machine learners, owned by Google LLC. You can find top-ranked companies like Amazon, Facebook, Google, and Microsoft posting their job Openings here. Members can subscribe to the latest updates on job openings and post their own vacancies.

Icrunchdata
Icrunchdata is dedicated to technology and data/analytics related jobs. You can search for jobs based on keywords, location, or skills you can also create a job alerts which will directly delivered into your inbox. The site also provides the latest news and trends within the data science space.

Upwork
Upword, formerly Elance-oDesk, is a global freelancing platform where businesses and independent professionals connect and collaborate remotely. You can find all types of jobs posted on this platform, but if you’re thinking about pursuing a freelance career as a data engineer or a data analyst, Upwork can prove to be a valuable resource.

Statsjobs
Statsjobs was the first job site of its kind to focus entirely on the job market for data analysts, data scientists and statisticians. The key feature is it posts jobs form reputable companies like Google, Pfizer, Siemens. Its updated regularly with data science and statistics-related jobs from around the world. If you find any post interesting, you can apply for the job directly without registering for a new account.

Top courses available to become a data scientist?

Data science certification from Harvard University (edX)

Total course: 9
Duration: 1 year
Rating: 4.8/5

This certification program from harvard will help you learn data science essentials, including R and machine learning using real-world studies to kick start your data science career.

Course include R basics, Visualization, Probability, Inference and Modeling, Machine learning, productivity tools, linear Regression, Wrangling.

What you will learn:

covered fundamental R programming skills
Become familiar with essential tools for practicing data scientists such as GitHub, Rstudio, Git, Unix/Linux
Statistical concepts such as Probability, inference and modeling and how to apply them in practice
Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
Implement machine learning algorithm
In-depth knowledge of fundamentals data science concepts through motivating real-world case studies.

Data Science and Statistics Certification by MIT (edX)
Total course: 5
Duration: 1 year
Rating: 4.6/5

Data Science and Statistics Certification by MIT helps you strengthen your foundation of data science, statistics and machine learning. journey will begin from the very basics of probability and statistics before moving on to data analysis techniques and machine learning algorithms.

Prerequisite: college level calculus, mathematical reasoning, and python programming proficiency to make the most of this certification.

What you will learn:

  • Master the foundations of data science, statistics, and machine learning
  • Analyze big data and make data-driven predictions through probabilistic modeling and statistical inference; identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making
  • Develop and build machine learning algorithms to extract meaningful information from seemingly unstructured data; learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks
  • Finishing this MicroMasters program will prepare you for job titles such as: Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer

IBM Data Science Professional Certificate

Total course: 5
Duration: 6 months
Rating: 4.6/5

Kickstart your Career in Data Science & ML. Master data science, learn Python & SQL, analyze & visualize data, build machine learning models.

This program consists of 9 courses providing you with the latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets.

Prerequisite: college level calculus, mathematical reasoning, and python programming proficiency to make the most of this certification.

What you will learn:

  • Well designed content and all the topics are covered elaborately.
  • The instructor helps you to work on the fundamental techniques with the help of examples.
  • Plenty of opportunities to implement the skills covered in the lessons and using real-world tools and real-world datasets.
  • No prior programming or computer science knowledge is required as all the topics are covered from scratch.
  • The classes contain tips and techniques along with assessments and projects.

Data Science by UC San Diego

Total course: 4
Duration: 10 months
Rating: NA

Gain the critical skills needed to become a data scientist, rated one of the best jobs in America and in demand globally.

In this MicroMasters program, you will develop a well-rounded understanding of the mathematical and computational tools that form the basis of data science and how to use those tools to make data-driven business recommendations.

Prerequisite: college level calculus, mathematical reasoning, and python programming proficiency to make the most of this certification.

What you will learn:

  • How to load and clean real-world data
  • How to make reliable statistical inferences from noisy data
  • How to use machine learning to learn models for data
  • How to visualize complex data
  • How to use Apache Spark to analyze data that does not fit within the memory of a single computer

Frequently Asked Questions Related to Data Science Career

Are data scientists in demand?

“Data Scientist” is the hottest profession of 2019 according to job-listing data.

According to the Harvard Business Review, the role of data scientist was described in 2012 as. “The sexiest job of the 21st Century.

IBM recently published a study that found that the world needs another 28% data scientists worldwide by 2020 to meet increased demand.

As far as who is hiring data scientists the top 20 companies with the world’s most successful businesses. It includes Amazon, Apple, Walmart, Facebook, Accenture,
Google etc. IBM study found that 59% of all data science jobs are in Finance and Insurance, Professional Services and IT.

Does data science require coding?

“Nope” If you Google this you’ll get a hundred different answers. Search the job listings and you won’t find a definitive answer there either. So the can you be a data scientist without knowing how to code? The answer loud and clear “NOPE”. Rachael Tatman, writing on freecodecamp states that every data scientist should be able to “Write code for statistical computing and machine learning. Programming language like Python, perl, C/C++, SQL and Java and tools like ASA, Hadoop, Spark, Hive, Pig.

Is data science the next big thing?

How would you define a data scientist and data science?
Data science is a concept used to tackle big data and includes data cleansing, preparation, and analysis. A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets. They understand data from a business point of view and are able to provide accurate predictions and insights that can be used to power critical business decisions.

A data scientist are Pros at interpreting data, but also tend to have coding and mathematical modeling expertise. Most data scientists hold an advanced degree, and many actually went from data analyst to data scientist. They can do the work of a data analyst, but are also hands on in machine learning, skilled with advanced programming, and can create new processes for data modeling. They can work with algorithms, predictive models, and more.

How is data scientist different from software engineer?

A data scientist are Pros at interpreting data, but also tend to have coding and mathematical modelling expertise. Most data scientists hold an advanced degree, and many actually went from data analyst to data scientist. They can do the work of a data analyst, but are also hands on in machine learning, skilled with advanced programming, and can create new processes for data modeling. They can work with algorithms, predictive models, and more.

A software engineer is a person who has a knowledge and applies the disciplined, structured principles of software engineering to all the levels – design, development, testing, maintenance, and evaluation of the software that will avoid the low quality of the software product. Software engineers also knows how to create and maintain IT infrastructure, large-scale data stores as well as cloud-based systems.

What is the difference between data scientist and data engineer?
A data scientist are Pros at interpreting data, but also tend to have coding and mathematical modeling expertise. Most data scientists hold an advanced degree, and many actually went from data analyst to data scientist. They can do the work of a data analyst, but are also hands on in machine learning, skilled with advanced programming, and can create new processes for data modeling. They can work with algorithms, predictive models, and more.

Data engineers are the data professionals who prepare the “big data” infrastructure to be analyzed by Data Scientists. They are software engineers who design, build, integrate data from various resources, and manage big data. Then, they write complex queries on that, make sure it is easily accessible, works smoothly and their goal is optimizing the performance of their company’s big data ecosystem.

What is a data scientist salary?
Based on Linkedin report Indian start Ups are very keen to build their analytics talent base. And they are willing to splurge for it. Start-ups are paying average salaries of Rs 10.8 Lakhs to data scientists. This is 12.5% higher than the average salaries paid by their larger counterparts.

Based on payscale the average salary for a Data Scientist, IT in india is Rs 706,283

Based on glassdoor the average salary for Data Scientist in India is 1029k/yr

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