Data Science jobs revolutionized the IT Industry by providing innovative solutions with better data insights and future predictions to complex business problems using aggregated data. We have data being generated at huge volumes, variety, and velocity from high-end technology, Internet Of Things (IoT), and Cloud Computing which is difficult to be handled by legacy BI and Database system tools.
What is Data Science?
Data-driven Science uses Algorithms to detect and analyze patterns that improve and solve complex business problems and conclude business decisions by predicting future trends and/or risks.
Drew Conway first depicted Data Science as a blend of Applied Mathematics, Statistics, Programming and Domain expertise.
Data Science Jobs – How Did it All Begin?
Data Scientist role listed as “The Sexiest Job of the 21st Century” highlighted LinkedIn’s use case of an improved business with Data Science insights and prediction statistics. This led to a huge demand for Data Science jobs with an exciting career and a promising salary (average base pay of INR1,032K/yr) LinkedIn and Glassdoor reported Data Scientist as the top demanding job role of the year 2019.
Data Science Jobs and Skills Classification
DATA ANALYST (PROGRAM ANALYST | FUNCTIONAL ANALYST)
As a Data Analyst, you must make sense out of data. You would prepare, process and interpret data in the best reports and visualizations to get actionable business insights.
Data Analyst Job Description
- Data Mapping and Reviewing Business Requirement Document
- Perform Data profiling on dataset author Business Requirements for BI
- Reporting Analyze and drive key business decisions Design, create and manage RDMBS
- Produce qualitative and quantitative data for AIDesign Algorithms- build high-quality Data solutions, tools, and capabilities data Analysis, mining, processing, and visualization to meet customer needs
Skills Required to Become a Data Analyst
- Data Governance Tools: Collibra or Manta Tableau, Excel, and SAP Business objects Web Intelligence
- Data Analytics and Statistics (Business role)Deep Machine Learning (ML) – supervised + unsupervised Modeling techniques
- Big data technologies – Hadoop, HiveR, Python, ScalaExcel Workbook, formula, and functions, SPSS, SAS SQL, Data Modeling, ETL development, Data Warehousing, and Visualization
- Quantitative and Data IntegrationProgramming, Data Mining, Analysis, Visualization, Statistics, Natural Language Processing (NLP)
Transform Data from User Story to Deliverables Design, build, manage and test Data Infrastructure and administration and own data sets to implement optimal consistent Data pipelines.
Data Engineer Job Description
- Build Infrastructure and drive Architecture and Technology Solutions
- Requirement Analysis, Design, Development, Unit Testing, System Integration Testing, and User Acceptance TestingAgile and DevOps methodologies.Scripting + Automation of manual tasks for onboarding, monitoring, and maintenance of services
- Operational excellence in root cause analysis and continuous improvement in Big data. Large scale software infrastructure systems/projects in a production environment
- Coordinate with the Product Management team, UI Engineers, Software Engineers, Data Analysts, Data Scientists and Customers in an Agile environment
- Develop new data engineering patterns. Design and develop complex ETL pipelines in BI Solutions and Data Warehouse platform to deliver “Data As Service” business solutions.
- Implement Algorithm evaluation methods.
- Analyze and build data tools and deep-dive failure analysis
Skills Required to Become a Data Engineer
- Data Modeling, Data Mining, ETL Development, and Data Warehousing
- Agile Methodologies, SCRUM, and Git VCS Complex SQL queries, Performance Tuning, Data Science, and ML tools
- Automation – Ansible, Chef, and/or Puppet
- Containerization – Kubernetes and Dockers CI/CD tools – Jenkins, GoCD
- Data Engineering Certification (e.g IBM Certified Data Engineer) SQL queries (Postgres)BI
- Reporting tools (Tableau, Business Objects, Cognos)DBMS fundamentals and data storage principles
- Big data – Hadoop, Hive, Hbase, Spark, EMR, etc.
- Strong Programming skills like Python, Java, Scala, R, and/or C/C++5+ years in SDLC, Data Engineering, Business Intelligence, Data Science or related field
Data Scientist qualifications include understanding data, Stats, and Math, applying Machine Learning (ML), with a good programming language. Expertise in designing and building ML Models for future predictions using past aggregated data.
Data Scientist Job Description
- Generate Analytical BI report on Data exploration and Analysis projects
- Run long-term (2-3 months or longer) BI and/or ML-based solution development projects to provide self-service analytical tools to the organization
- Lead team of Data Scientists to Analyze Big Data and build Models and Algorithms
- Develop high-quality ML models from complex data sets
- Design and develop highly scalable reliable data pipelines (ETL)Complex SQL queries
Skills Required to Become a Data Scientist
- Modeling framework – SciKit Learn, TensorFlow, SAS, R, MATLAB
- Product strategyConternization – Kubernetes, DockerCloud Server Architecture + components, integration with OS + cloud environment
- Hardware programming. Python analytical packages (Pandas, PySpark, statsmodels, scikit-learn, matplotlib), SAS, MatLab, Scala, Jupyter notebooks
- Complex SQL, programming, and Scripting – Python, R, PHP or PerlDeep ML (TensorFlow), Data Mining, Data Modeling, Data Science, Statistics, Analytics, NLPBig Data technologies
MACHINE LEARNING ENGINEER
Programmers who develop scalable and robust ML Algorithms for optimized solutions
Machine Learning Engineer Job Description
- End-to-end leadership of a project, (data mining -> modeling -> production deployment)
- Own Current and Future ML InfrastructureDesign, code, train, test, deploy and iterate on large scale ML systems
- Build and ship machine translation technology to real-world users
- Design and deploy real-world, large-scale, user-facing MT systems
- Prototype, develop and experiment with ML-based techniques
- Translate Product requirements into engineering goals and tasksGuide data collection, labeling, sampling and filtering for training and testing the system
- Design, develop, test and debug low latency, high throughput systems for an end-to-end production-grade stack.
Skills Required to Become a Machine Learning Engineer
- Deep ML – supervised and unsupervised Modeling techniques
- Data pipelines or Distributed Message Queues
- Deployment at large-scale on AWS or similar IaaS services
- Go or Ruby4+ years building ML or AI systemsDeep-learning toolkits – Tensorflow, PyTorch, CNTK, and DynetGitDockerMT/NLP toolkits – Moses, SRILM, Marian, T2T, Sockeye, etc.Programming and Scripting – Python, R, PHP or PerlDeep ML, Data Mining, Data Modeling, Data Science, Statistics, Analytics, NLPBig Data – Hadoop/Spark
Data Science skills/tools
The tools are role and company-specific; with the most popular tools:
- R – Programming language for Statistical computing and graphics
- PYTHON – Open-source, a high-level programming language widely used in Scientific and Numeric computing.
Important Libraries, Graphical and Statistical packages –
- SciPy – Library for Scientific tools ( modules for linear algebra, optimization, integration, special functions, signal and image processing, statistics, genetic algorithms, ODE solvers, etc)
- Matplotlib – Library to generate plots, histograms, power spectra, bar charts, error charts, scatterplots, etc out of complex data
- Pandas – High-performance data structures and analysis tools
- TensorFlow – Framework for Machine Learning
- NLTK – Excellent platform for Natural Language Processing
- SQL – Structured Query Language to store and query data stored in DBMS.
- SCALA (SCAlable LAnguage) – Object-Oriented and functional language integrated with Java used for Analytics
- HADOOP – An open-source Apache Software for reliable, scalable and distributed computing of big data.
- TABLEAU – Visualization Software tool specializing in Graphical Analysis of data
- WEKA – Machine learning and Data Mining tool
- Jupyter Notebook – An open-source WebApp for data cleaning and transformation, numerical simulation, statistical modeling, data visualization, ML, and many more data science analysis
Resume building – key points
|Format and body||Precise, crisp and clear, customized to the applying job description|
|Personal details||Accurate details along with a personal number, email id, & LinkedIn link|
|Subtitle||Job title of the role you are applying for | current role | key technology|
|Synopsis / summary||Present job description, skills, and key-values you can bring in|
|Skills||Bullet out the skills with the strongest(and/or relevant) ones on the top|
|Experience||Recent experience at the top with a clear timeline and job title.Job description and how you achieved them with what skills and tools|
|Projects||The main section to showcase your project from a real-time dataset|
|Education||Basic education including online courses and higher studies|
|Publications/ Certifications/ Awards/ Recognition||List your unique assets in this section|
How to build a Data Scientist resume walks you through each step.
Where to apply for jobs
In addition, Data Science Jobs Websites provide real-time datasets:
- Amazon Jobs
- Big Data Jobs
- Analytics Jobs
- Built In
- Data Elixir
- Analytics Vidhya
The most preferred way is to search and apply to the targeted Company’s websites.
Data science jobs career progression and next role
Data science jobs cover all aspects of mathematical, compositional, programming, statistical, analytical, and ML techniques, one is needed to be “Jack of all trades, master of some”
Gain Industrial knowledge and real-time project experience with this Data Science Online Learning Program.
Eventually, you may also want to gauge your knowledge by checking out the most popular Data Science Interview questions and answers.