Data Science is an interdisciplinary field of scientific algorithms, tools, and technologies to extract useful insights from the data. This blog post lists the 10 data science career paths with job responsibilities and required skills, tools and technologies.
Data science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.
http://blog.codoplex.com/introduction-data-sciences-notes/
According to Zeeshan-ul-Hassan Usmani, the following is a list of 10 data science career paths with required skills, tools and technologies.
- Data Science Developer
- Machine Learning Developer
- Data Scientist
- Data Engineer
- Data Analyst
- Data Scavenger
- Data Visualization Expert
- Data Story Teller
- Big Data Expert
- Data Science Researcher
10 Data Science Career Paths
| # | Title | Responsibilities | Required Skills |
| 1 | Data Science Developer | To implement the algorithms using programming languages | Python, R, SQL, Jupiter Notebook, RStudio, etc. |
| 2 | Machine Learning Developer | Machine Translation, Deep Learning, Computer Vision | Scikit, NLP, Tensorflow, Feature Extraction, Classification, etc. |
| 3 | Data Scientist | To implement models for future predictions | A/B Testing, Causal Inferences, Co-Relation, Pattern Matching, etc. |
| 4 | Data Engineer | To make sure that the data is ready to be fed into ML algorithms/ Models | Prepare data pipelines, Auto feeding of data to ML algorithms, ETL, SQL, etc. |
| 5 | Data Analyst | To clean and analyze data | Data cleaning, Data Wrangling, Data Classification, SQL, ETL, etc. |
| 6 | Data Scavenger | To collect and prepare data | Data Collection, Web Scrapping, Data Repositories, Data Extraction, Selenium, URLLib, Scrappy, etc. |
| 7 | Data Visualization Expert | To display data in a meaningful way | Seaborn, Tableau, Plotty, Matplotlib, Qlikview, etc. |
| 8 | Data Story Teller | To present the data like a useful story for evaluation of the commercial analytical value of the data | R, Matplotlib, Data Visualization, etc. |
| 9 | Big Data Expert | To extract and analyze huge amount of data | Scala, MongoDB, ApacheSpark, Hadoop, Weka, R, etc. |
| 10 | Data Science Researcher | To find new possibilities, algorithms, tools, and technologies to efficiently and effectively utilize data | Math, Statistics, Linear Algebra, Calculus, Data-Driven Policy Making |
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