Data scientist career path and opportunities
It is the lucrative career choice you should think to pursue. With its $123,000 median salary, Glassdoor named it as the number one job position in the market with the highest satisfaction score of 4.2 hits out of 5. Harvard called it the sexiest job of the 21st century. But let us consider all the other reasons why one should consider data scientist career path as the most in demand?
 Big data is becoming more valuable as online digital connectivity continues to soar. Digital systems are leaving behind traces of big data from various domains that connect consumers and businesses or organizations.
 Most big organizations store multiple petabytes of data from various business circles. The process to refine this data into meaningful information would involve a âmashupâ of several analytical efforts that require skilled personnel. If handled strategically, big data can produce valuable information that informs and fuel the business/organization productivity.
 A data analyst uses systems developed by data engineers/architects to mine (through analytics) big data to generate insights that propel and improve the business decision-making and profit gains.
Data scientist career path: the lucrative job of the 21st century.
 There will always be big data opportunity and the trends of data scientists and analysts are soaring high day after day. Harvard business review named data scientist as the sexiest job of the 21st century.
 There are lot of enthusiasm for big data especially those focusing on technologies that tame it as easy as possible. Think of Hadoop and related open source tools (frameworks for distributed file system processing), cloud computing, and data visualization among others.
 While these tools are breakthroughs in analyzing big data, there is a shortage of data scientists. There is a high demand for professionals with the skill set (and the mindset) to put these big data opportunities to good use. The demand has raced ahead of supply in some sectors.
 Data scientist career path. What does a data scientist do?
A hiring manager once said, âI need someone who understands dataâ. Itâs as simple as that and yet companies struggle to find the right match.
The demand for data science skills is disrupting the job market. As per IBM projects that by 2020, the need for all data professionals will increase to 2.72M jobs in the United States. Â
 To attract the right data scientists, organizations need to be able to clearly define what their business needs are. Data never stop flowing in this digital realm. Data scientists are able to bring structure and analysis to large quantities of formless big data. They need to identify rich data sources, join them with other potentially incomplete data sources, and clean the resulting set. The end results should be a stream of flawless, seamless orders of meaningful information that provides business with the right direction.
While data scientists discover news paths for big data analysis, they are faced with technical limitations but most often never bog down their search for innovative solutions. They always find the productive path to fashion their own tools with prospects of conducting their analysis more effectively while minimizing the cost.
 Data scientist make discoveries, communicate what theyâve learned from their systems (including big data), and suggest its implications for new business directions.
 Their skills involve being creative in displaying information visually and making the patterns they find clear and compelling for the end user. At its highest realms, they advise executives and product managers on the implications of the data for their products, processes, and decisions.
At the bottom line, a data scientist job implies some statistics & modeling knowledge (from math and data visualization), combined with programming skills (including database languages) that ultimately result in actionable insights for businesses decision-making.
Finding the right data scientist career path
Data science career path involves diving deep into the data science pipeline (objectives, process etc.), roles, and job opportunities.
The main objective of a data scientist is to go through a sequence of steps in a systematic manner to achieve the desired results. Each step is a contributing factor that involves the creation of models, their validation, evaluation, and potential refinement of big data into the final results. The output must be insightful information presented in charts, graphics, and other information representation forms thatâs structured, compelling, and easy to interpret by the managers and executives.
 Business objective for the data scientist involves identifying business issue and/or attractive market opportunity, clearly understand whatâs to be accomplished in order to help the business gain a competitive edge.
From that scenario, the role of a data scientist is not always technical. They donât just program and perform statistics at its core stages of big data process. They need to possess contextual skills (as data analysts) for planning and reporting from all data analytics stages.
Data scientist career path: Roles
A data scientist makes value out of big data. They proactively fetch information from various sources (as inputs) and analyzes it for better understanding (information) about how the business performs, and builds AI tools (software) that automate certain processes.
 Data scientists are multi-talented professionals, their role is a crossover between many different disciplines.  They can be programmers, statisticians, analysts, as well as being good data communicators. If youâre passionate about career path of a data scientists, there are many opportunities you can get there.
 Since the data science field is broad and often involves a lot of confusions. The definition of the job and its roles is convoluted. Data scientist roles is a mix of various occupations like big data engineer, data software engineer, hackers, data analyst, business intelligence (BI) analyst, marketing analyst etc. Their expertise in their roles depends on the scope of the job requirements.
 Here are some of the roles/ job description of the data scientist.
Help the company discover the   information hidden in vast amounts of big data
Help the company make smarter decisions   to deliver even better products and/or services. They interpret and manage   data and solve complex problems using expertise in a variety of data   niches
Focus on applying data mining   techniques, doing statistical analysis, and building high-quality   prediction systems integrated with the company products the services.
Build systems that help the company   achieve their goals by using machine learning techniques and so on.
Improve and extend the features of big   data systems used the company for better data analysis and improved   decision-making.
Develop internal A/B testing procedures   and a lot more.
Data scientist career path: How to become a data scientist
Now that youâve had some go forward ideas of what a data scientist can do, the big question becomes â how to become a data scientist.
Profession research and job description/roles
First, before you begin any career, researching the profession thoroughly allows you to get a clearer picture of how to get involved.
Remember to clearly understand the job description â data scientists are required to use algorithms and statistical techniques to turn big data into insightful information. Â Have a knowledge of the industry the hiring company is operating in. keep in mind that as a data scientist, you must possess effective communication skills. You must be able to communicate information in effectively as it should be.
 Data scientist career path: Key Responsibilities
Responsibilities of a data scientist depend on the organization youâre working in. according to Toptal, among other responsibilities, key data scientists responsibilities may include:
Selecting features, building and   optimizing classifiers using machine learning techniques
Data mining using state-of-the-art   methods
Develop machine learning models and   analytical methods.
Extending the companyâs data with   third-party sources of information when needed
Enhancing data collection procedures to   include information that is relevant for building analytic systems
Processing, cleansing, and verifying the   integrity of data used for analysis
Doing ad-hoc analysis and presenting   results in a clear manner
Creating automated anomaly detection   systems and constant tracking of its performance
Data scientist career path: skills
Data scientistâs career path entails wrangling with big data. They apply all their analytic skills to uncover hidden data solutions to business challenges. Itâs a heavy task that needs a huge amount of structures and unstructured data paints. They must clean, massage and organize data with their formidable skills in statistics, programming, and math.
 According to data-flair training, a data scientist utilize their knowledge of statistics and modeling to convert data into actionable insights about everything from product development to customer retention to new business opportunities.
 They must possess both technical and non-technical skills to perform their job in an effective manner. They must apply the tools needed to capture data, for data pre-processing, data analytics & pattern recognition, as well as data presentation and visualization.
 Examples of data scientist skills;
Must learn about SQL engines like Apache   Hive, Impala, Spark-SQL, Flink-SQL etc.
Knowledge of big data technologies. Get to know the first generation of tools like Apache Hadoop and its ecosystem like Flume, pig, hive, and so on.
Python â an interpreted, object-oriented programming language with dynamic semantics.
Knowledge of statistical data analytics   language R (highly recommended)
Skills on machine learning algorithms for advanced data analytics, productive analytics, advanced pattern matching and so on. Machine learning tools are available in the market like weka, nltk, etc.
Advanced skills in data visualization tools like Tableau, JMP. R also has support for data visualization (such   as ggplot2, lattice, rCharts, google charts, shiny for web apps for   presentations, etc.)
Non-technical skills include excellent communication skills, Business acumen and analytical problem solving to get optimum output.
Data scientist career path: Certifications
There are excellent data scientist certification programs that are widely recognized and reputed if youâre looking to land at the big companies that hire professional data scientists.
 Here are few Data Scientist certifications that focus on useful skills:
Cloudera Certified Professional: Data   Scientist (CCP: DS). CCP: DS is aimed at data scientists to demonstrate advanced skills   in working with big data. Candidates are drilled in 3 exams â Descriptive and Inferential Statistics, Unsupervised Machine Learning, and Supervised   Machine Learning. All candidates must prove their skill set by developing   a production-ready data science solution under real-world conditions. Here   is everything you need to know about Cloudera Data Scientist   Certification.
Certified Analytics Professional (CAP). Created in 2013 by   the Institute for Operations Research and the Management Sciences   (INFORMS) for data scientists. Their certification includes the framing of   business and analytics problems, data, and methodology, model building,   deployment and lifecycle management.
EMC: Data Science Associate (EMCDSA). Demonstrate the ability to apply common techniques and tools required for big data analytics. Candidates are judged on their business acumen and technical expertise in tools such as âRâ, Hadoop, and Postgres, etc.
Data science is always âin demandâ as companies begin to realize the importance of their data analytics to make informed decisions. There is always the look-out for talented data scientistâs professionals from all work industries.
 Data scientist career path involves building a strong professional network. Join online data scientist forums and competition platforms such as those hosted by Kaggle, Topcoder and the Defence Science Technology Laboratory (DSTL). Keep watch of top data science site including Data Science Central, SmartData Collective, What's The Big Data, insideBIGDATA and so on.
 Be on the lookout on job listing sites like data scientist jobs, KD Nuggets, Kaggle, and much more.
 If you are looking for a freelance data analyst projects or a data analyst jobs, you can register to Econolytics i.e. a data analyst marketplace where you can find the data scientist freelance projects.
