I had attended a webinar recently and learnt something incredibly unique! I discovered that you could be a Data Scientist, but having a #specialisation is IMPORTANT!

What is a "specialisation"? How many kinds of specialisations are there in #DataScience Domain?

Thread🧵 Image
*Specialisation*
One can acquire all the skills of a Data Scientist, but having specialisation in a particular skill can set you apart from the rest. It can be anything! You can analyse data as no one else; data visualisation or database management (#DBMS) could be your niche.🤩
☑️Data Visualisation
If you have a knack for producing beautiful graphical representations from the data, this could be your domain of specialisation, and you could become a Data #Visualisation Engineer. Image
☑️Data Engineering and Data Warehousing
If you think you can clean, transform and extract data from various sources efficiently, you can opt for the following roles: #Data Analyst, #Database professional, Data #Engineer. Image
☑️Strategy Making or Business Intelligence
Suppose you think you can develop great solutions to a business problem and formulate strategies to boost the business. In that case, you can opt for the following roles: BI Engineer, Data Strategist, BI Analyst, BI Developer. Image
☑️Data Mining
If you think you can EXPLORE the data, draw insights from them and predict trends, you can opt for the following roles: #Statistician, Data analysts, Business Analyst. Image
☑️Machine Learning
If you think you can write an optimised code for #ML Algos, perform A/B Testing, train models, deploy the trained models, and build data pipelines, this specialisation is for you. The roles include ML Engineer, Researchers, #AI Specialist, Cognitive Developer. Image
☑️Cloud Computing & #Architecture
If you know how to design and implement infrastructure that requires cloud computing & know how to host ML models on a cloud platform, then this specialisation is for you. The roles include Cloud Architect, #Cloud Engineer, Platform Engineer. Image
☑️Operations Data Analytics
This role doesn't require you to possess high technical skills. You can be a problem-solver and know how to use data #analytics tools to draw insights and predict trends from the data. The roles include Planning Analyst, Decisions #Analyst.
☑️Market Data Analytics
This role involves measuring, analysing, and managing marketing performance for effectiveness and optimise return on investment. It requires an understanding of the market #trends, customer needs & preferences.
Job roles: Product Analyst, Market Analyst

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More from @Sukriti_Macker

4 Jun
SQL is a crucial skill for a Data Scientist! I would be releasing everything I know, little by little, and then a compiled version of all the threads you could revisit.

A Beginner's Guide to MySQL basic queries 😎 -Part 1 (Creation of Everything!)

Thread 🧵
What is a database?
An organised collection of data that can be interrelated & makes operations like retrieval, insertion & deletion of data efficient.
User can perform different queries to perform an action based on their requirements.

Types:-
Hierarchical
Network
OO Database
A relational database is a type of database that stores everything in relations or tables.
Tables have columns and rows.

SQL -> Structured Query Language used to interact with a relational database.
Read 8 tweets
31 May
You want to step into the field of DATA. But do you know which area is best suited for you?

Compare your skills with the domain you are interested!!
Thread🧵
☑️Data Analyst
If you don't know what role Data Analyst plays, check out this (cutt.ly/VndBSnI)

Skills:-
Python and R
SQL
Excel
Data Visualisation Tools 📊📈📉
Report Building Tools (like Power BI & Tableau)
Communication, Presentation & Critical Thinking skills
☑️Machine Learning Engineer
If you don't know what role Machine Learning Engineer plays, check out this (cutt.ly/VndBSnI)
Read 6 tweets
29 May
Data Analytics is the fundamental step towards building a career in Data Science. It uses pre-existing data or historical data to solve a given problem. It includes tasks like checking hypothesis, creating dashboards & reports and gathering data from various sources.
One should be adept at working with tools like Power BI to generate reports, Microsoft Excel and SQL to work with structured or unstructured data, and proficient in R and Python.
Data Science is a multidisciplinary field. It includes Computer Science, Mathematics, Statistics, Economics, etc. It is NOT a subset of Machine Learning. Data Scientist may use machine learning to discover new insights from the data, but not necessarily.
Read 6 tweets

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