Nov 1, 2020
An MBA’s guide to understanding Data Science
If you come from a non-technical background, it is easy to feel insecure with heavy buzzwords around data science. Do not let that concern overshadow all that you can bring to the table. A business-savvy tech person or a technically-sound business person is an asset to any organization.
To oversimplify it,
“Data science is the discipline of making data useful.”
If you are wondering why you should care about these terms, we present to you some facts.
- The big data analytics market is expected to touch $103 billion by 2023.
- In 2020, an individual will generate 1.7 MBs in a second.
- Users on internet generate about 2.5 quintillion bytes of data each day.
- 95% of businesses acknowledge the need to manage unstructured data as a challenge for their business.
- 97.2% of organizations are investing in big data and AI.
- Using big data analytics, Netflix saves $1 billion per year on customer retention. [1]
- In 2020, there will be around 40 trillion gigabytes of data (40 zettabytes)
Organisations are extensively hiring data scientists and machine learning engineers for an analytical transformation within the various verticals to keep up with ever changing and rapid data generating world.
“You can’t manage what you don’t measure.” — Peter Drucker
Thee is also a high demand for business translators or those who can serve as a link between data analysts and practical applications and solutions to an organization’s business challenges.
Increasing number of organizations are on the hunt for these translators who not only possess data savviness but who can also deeply embed themselves into the organization’s decision-making processes. McKinsey & Company estimated the demand for translators will reach 2 to 4 million in the United States alone over the next decade.
Businesses are swimming in data of customer behavior, competitors, and their operations. The need for managers who can sail through the data and uncover insights that would generate profit is why data science positions are some of the most in-demand and highest paying jobs around.[2]
A Data Science Lifecycle
Evolution of Data Science
The Begining (40s, 50s, 60s)
Used in operation Research during World War II, predicting Weather Model on ENIAC, Barometric equations,Scheduling and resource allocation
Analytics turns Mainstream (70s, 80s)
In 1977, John W. Tukey published Exploratory Data Analysis, the book emphasised on using data to suggest hypotheses tests.
The Relational Database is born!
1982: IBM DB2, Oracle v3, Sybase (SAP)
1986: First standardized SQL
1987: Commercial use of Decision Support Systems in the Texas Air Traffic Expert system
The Internet goes Global (90s)
1995: Amazon, eBay
1996: HotMail
1998: Google, Paypal
Data warehouses and ETLs come into existence in 90s
Analytics (OLAP): Long queries, aggregations, data mining, reporting, models Operations (OLTP): Fast transactions, ACID, consistent, available, fault-tolerant
The World goes Social (00s)
Hyper growth of web applications
2003: LinkedIn, Skype
2004: Facebook
2006: Twitter
Map-Reduce and Hadoop come into existence for processing huge raw files.
Fast Data, APIs, Mobile and IoT (10s)
Spark is a new framework for in-memory computing. It combines SQL, streaming, and complex analytics.
Rise of distributed Computing paradigm: SQL, Machine Learning, Map-Reduce, Graph Analytics
Micro-Batch and Event Streaming Analytics (20s)
Micro-Batch (Spark Streaming)
Log Oriented (Kafka, Samza)
NewSQL (VoldDB)
Machine Learning Scientist: Often going by titles of Research Scientist and Research Engineer, they are the ones who research new approaches and build new algorithms and prototypes, using mathematics in order to solve business problems.
Data science consultant: Are you someone who has a special knack towards data analysis, problem-solving and has an in-depth industry knowledge, not forgetting to have attention to detail and advanced vision? Then probably this profession is for you!
Data science consultants provide data science solutions to their clients and help them understand the business mechanisms with in-depth understanding and insights. They also strive to build up their client’s analytical skills and data competencies and sail them through each step in the process of hypothesis testing.
Product managers: this job role requires the person to be innovative, drive business requirements and communicate their decisions lucidly. In the data science domain, a product manager would need to discern applications based on the inferences that has been assimilated from the data. They are a wholesome pack of managers and technicians and need to talk to everyone, right from designers to engineers and jence can be pursued by an MBA degree holder.
Product analysts: When it is time for a company to design or release a new product, product analysts come to their rescue! Their main task is to provide continuous product analysis, conduct thorough market research and develop marketing strategies. They engage in inter-departmental collaboration and work in tandem with the entire product development team and other several entities simultaneously. Their other responsibilities would include creating dashboards, defining product metrics, and performance control and measurement.[3]
References
[1] https://techjury.net/blog/big-data-statistics/#gref
[3] https://www.jobhero.com/career-guides/interviews/prep/what-is-a-product-analyst
[4] https://datasciencedegree.wisconsin.edu/blog/history-of-data-science/
