According to an annual report by CNBC “Here are the best and worst jobs of 2019” the report is based on income, anticipated growth rate, work experience and stress. The ranking shows not only continued demand for workers with a STEM background – Science, technology, engineering and mathematics.
Based on the report “STEM fields have pretty much consistently been at the top of these reports, but the numbers aspect is interesting because so many industries need those positions,” said Kyle Kensing, online content editor at CareerCast.
Another article by indeed on “The Best Jobs in the US 2019” This list of the best job 2019 based on experience, fastest growth and offering the highest pay.
Last but not the least Linkedin unveiled the Most in demand skills of the year 2019. based on the LinkedIn data, these positions come with high salaries, a significant number of job openings and year-over-year growth.
What is Data Scientist?
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining. ~Wikipedia
Data Scientist is a new breed of analytical data expert who analyses and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision-making.
In simple words a Data Scientist is mathematician, a statistician, a computer programmer and analyst equipped with a wide range of skills set, balancing knowledge in different computer programming languages with advanced experiences in data mining and visualization.
Why we need data science?
In the past, the data that we had was mostly structured and smaller in size unlike data in the traditional systems which was mostly structured. But now today most of the data is semi-structured or totally unstructured. At present 80% of the data is unstructured and these data are generated from different sources like multimedia forms, large text files, financial logs, sensors and instruments.
To sort this massive data we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.
What is an Average Salary of Data Scientist?
Based on the annual report by Burtch works study of data scientist, Forbes released an article on the salaries of data scientists 2019. According the report The median base salaries range from $95,000 at level 1 (0-3 years of experience) to $165,000 at level 3 (9+ years) for individual contributors and from $145,000 at level 1 (1-3 reports) to $250,000 at level 3 (10+ reports) for managers.
As per the PayScale, the average Data scientist salary in India is 619182 per year.
According to a report by indeed, the annual average salary of a data scientist in India is 834,901 per year. The report is based on salary estimated form 563 employees, users and past and present job advertisement on indeed in the past 36 months
Job Responsibilities of Data Scientist
Harvard Business Review called Data science “The sexiest job of the 21st century“. Businesses form start-ups to well-established fortune 500s are scrambling to fill these roles with the best and the brightest.
Here is the list of common responsibility for full stack developer
- Able to work with large volumes of activity & user data
- Ability to learn and pick up a new language, tool, platform quickly.
- Manage & Serve multiple analysis requests simultaneously.
- working closely with clients along with team handling the responsibility
- Responsible for designing and developing data-driven products and features.
- Deep interest in mining and analyzing data, looking for stories, patterns, trends and insights.
- Understand and apply algorithms related to text mining, images recognition, logistic, regression.
- Bring new and innovative ideas and approaches to develop business solutions.
- Should be able to independently test, implement and validate scoring models
- Build the hypothesis, identify research data attributes and determine the best approach to address business issues.
- Solving complex problems like fraud detection, auto screening, payment propensity, recommendation engines
- Interacts with company senior and managers to inform on industry trends and emerging research topics.
- Translate business problems into Analytical problem and analytical output to a business decision.
- Apply machine learning algorithms to improve the intelligence of products like recommendation engines, auto-generation of partner preference values.
What are the Key Skills to be a Data Scientist?
- Extremely curious and relentless at figuring out solutions to problems
- An industry experience, which demonstrates building business-relevant data science solutions
- Excellent knowledge of data cleaning and Extract Transform Load (ETL) pipeline for the model development
- Comfortable interacting with different kinds of databases and extract relevant data from the different databases
- Experience with building production-ready models and maintaining models in production
- Strong skills in undergraduate level statistics, linear algebra, modelling fundamentals, and probability
- Understanding of parallel computing and parallelizing computations using frameworks like Desk, Spark, MPI
- Familiarity with Deep learning and Deep learning libraries like Keras, tensorflow, CNTK, Theano, PyTorch, Caffe
- Knowledge of Natural Language Processing (NLP) fundamentals and familiarity with Python or R NLP stack
Knowledge of Machine Vision
- Understanding of Bayesian Modeling, Monte-Carlo Methods and tools like Stan, PyMC3
- Good knowledge of fundamentals of time-series modelling and Time Series stack in R or Python
- Knowledge of functional programming paradigm or have worked on functional languages like Scala
- Understanding of functional programming using F# (F-sharp)
- Prior experience working in the financial industry
- Experience in building econometric models
- Knowledge of code versioning tools
- Ability to work in a fast-paced high-pressure environment
- Strong skills in computational thinking (Numerical Analysis) i.e. translating math into units of codes using appropriate data structures with – Proficient in Python or R data science stackoptimal time complexity
- Detail oriented and efficient problem solver. Ability to synthesize a diverse set of information to prototype business solutions.
Should have an excellent verbal and written communication skills
Top rated courses for developing skills to get hired for a Data Science job:
List of Free and Paid Courses
1. Coursera: Data Science Specialization
An introduction to data science developed and taught by leading professors. Ask the right questions, manipulate data sets, and create visualizations to communicate results. This course covers the concepts and tools you’ll need to throughtout the entire data science path.
WHAT YOU WILL LEARN
Use R to clean, analyze, and visualize data.
Navigate the entire data science pipeline from data acquisition to publication.
Use GitHub to manage data science projects.
Perform regression analysis, least squares and inference using regression models.
2. edX: Data Science Essentials
This course is part of the Microsoft Professional Program Certificate in Data Science and Microsoft Professional Program in Artificial Intelligence. With a great demand for data science, talent edx and experts from Duke University and Microsoft help you develop your career as a data scientist.
What you’ll learn
Explore the data science process
Probability and statistics in data science
Data exploration and visualization
Data ingestion, cleansing, and transformation
Introduction to machine learning
The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning
1. Familiarity with basic mathematics
2. Introductory level knowledge of either R or Python
Duration: 6 weeks
3. DATAQUEST: Learn Data Science
Dataquest can teach you the data skills you’ll need whether you’re new to the field or looking to take a step up in your career. Learn Wide range of languages like Python, R, SQL, data visualization, data analysis, and machine learning.
With dataquest, you’ll be writing code and working with real-life data sets form your browser. you’ll get to interact with peers who will inspire and motivate you.
1. Meits: Introduction to Data Science
This course serves as an introduction to the data science principles required to tackle real-world, data-rich problems in business and academia.
The program covers
1. Data acquisition, cleaning, and aggregation
2. Exploratory data analysis and visualization
3. Feature engineering
4. Model creation and validation
5. Basic statistical and mathematical foundations for data science
Price — $750
familiarity with basic statistical and linear algebraic concepts such as mean, median, mode, standard deviation, correlation, and the difference between a vector and a matrix. Python is a requirement for the course as python v3 is currently used in the course
MicroMaster from edX is an advanced program in statistics and data science comprised of four online courses and a virtually proctored exam that will provide you with the foundational knowledge essential to understanding the methods and tools used in data science, and hands-on training in data analysis and machine learning.
Probability – The Science of Uncertainty and Data
Data Analysis in Social Science—Assessing Your Knowledge
Fundamentals of Statistics
Machine Learning with Python: from Linear Models to Deep Learning
Capstone Exam in Statistics and Data Science
Price – Free or $1,350 for credential and graded materials
Provider – University of Michigan