How to become a Data Scientist

By Adarsh(Data Science Enthusiast)

Skill Required for Data Science :

• Python or R Programming languages.
• Familiarity with libraries like Numpy, Pandas , Scikitlearn .
• Knowledge of Statistics, Linear algebra ,Probability and mathematical concepts.
• Familiarity with SQL, particularly in relation to functions, subquery and Joins.
• Knowledge of Machine learning Algorithms.
• Data visualization with Tableau/PowerBI
• Domain knowledge

Steps for creating a project in Data Science:

• Initially start with basic projects like doing fit/predict on CSV data.
• After gaining some knowledge, take some good research papers and try to implement it.
• You can either take a difficult project and implement it up to some extent or take an easy project and implement it with good results.
• Participate in Kaggle competitions and read the notebooks available by other experts on Kaggle.

How to enter Data Science roles :

• Getting a data science role is not an easy task for freshers. You can start with data analytics roles and later try to switch after gaining some experience.
• You can also look for masters in data science (CMI Chennai, ISI Calcutta ) or from abroad .
• You can look for entry level jobs in some startups

Resources :

• https://www.kaggle.com/
• https://youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
• https://youtube.com/@statquest

Future of Data Science:
The future of data science is mainly dispersed into two fields from job perspective.

• The research based roles are like research assistant, project assistant in some universities in India or abroad. Also, companies like Google, Amazon have their research institutes where you can aim for.
• Application based includes roles like Research and Innovation based jobs. Companies like TCS , Accenture have their AI units . Apart from that, product based companies also hire Machine learning engineers, AI engineers.

Additional Notes :

There is no specific definition for Data scientist role. It depends on company to companies what kind of job a data scientist will be doing. But in general, Data scientist mainly work with data to draw business insights out of it. Right from data collection to data preprocessing, making ML/AI models, drawing visualizations, creating algorithms for performing different AI based tasks like recommendation system, intelligent automation etc., a data scientist is mainly involved into these things.

By Subhash ( Data Scientist)

Skills required to become a data scientist:

• Proficiency in mathematics, including calculus, probability, permutation and combination, and linear algebra.
• Strong understanding of statistics.
• In-depth knowledge of Python for data science purposes.
• Familiarity with SQL, particularly in relation to functions, subquery and Joins. Focus on writing complex and long query
• Practical experience in Excel, with an emphasis on application rather than memorizing functions.
• Proficiency in at least one visualization tool, such as Power BI, Tableau, or Looker.
• Comprehensive understanding of machine learning algorithms and their mathematical foundations.
• Knowledge of classical natural language processing (NLP) for entry-level job opportunities.
• Proficiency in advanced topics such as deep learning and advanced NLP.

Steps to create a data science project:

• Begin by following tutorials on YouTube to create projects, writing code line by line.
• Once you grasp the project’s concepts, work with different datasets from sources like Kaggle, UCI repository, or others, building models and focusing on hyperparameter tuning for improved accuracy.
• After completing the above steps, explore and implement your own ideas within data science projects.

How to enter data analyst/data science roles:

• Follow the steps outlined above for data science roles. However, for data analyst positions, exclude machine learning, NLP, and deep learning from your learning path.
• Concentrate more on case studies and developing a solid understanding of the business context for data analyst roles.

Useful resources available:

• Explore Krish Naik’s YouTube channel: https://www.youtube.com/@krishnaik06
• Check out CampusX Channel: https://www.youtube.com/@campusx-official
• Read articles on platforms like Medium, Analytics Vidhya, and other relevant blogs that cover projects and concepts within the field.

The future of data science:

In simple terms, the future of data-related fields is directly linked to the availability of data. As long as there is data, there will be a demand for data science, data analytics, and data engineering. Additionally, emerging fields like prompt engineering are also generating opportunities as the field continues to evolve.

Additional Notes:

Additionally, it is crucial to emphasize the importance of problem-solving skills and the ability to understand and dissect complex problems. While it may seem ordinary, this skill is paramount in the field of data science. In today’s era, where code and AI platforms are readily available, what sets a data scientist, data analyst or data engineer apart is their aptitude for problem-solving and their capacity to identify the core problem and determine effective solutions.

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