Data Science is the most demanding job across the world right now. As the amount of data is increasing, every business is trying to use machine learning for their growth. Data Science has wide applications in different domains like Healthcare, Retail, Finance, Manufacturing, Logistics, Supply Chain, etc. In this article, I will try to answer all the different questions you can have if you are preparing for Data Science, Machine Learning, or any other related field.
What is Data Science
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data Science has wide applications like Recommendation Systems, Fraud Detection, Image Processing, Voice Detection, Target Customer, Cancer Prediction, etc.
Who is a Data Scientist?
Data Scientists are a new breed of analytical professionals who combine their technical skills with analytics to solve a problem. We can broadly divide them into three categories based on what they work in:
- Analytics
- Natural Language Processing
- Computer Vision
The last two categories belong to Deep Learning. In this article, we will discuss the first category. Data Scientist who works in Analytics mainly uses machine learning, visualization tools, and other analysis methods to solve a business problem.
There are many different profiles that look very similar to Data Scientists like Machine Learning engineers, AI developers, Data Analysts, Business Analysts, etc. There are differences among them but the core for all these profiles is the same which revolves around data.
Any Data Science project will involve the following steps:
- Understanding Business Problem
- Collecting Data
- Cleaning the Data
- Collect Insights from the Data
- Creating the Model
- Performance Evaluation
- Deploying the Model
- Parallel Communication with the Client at each stage.
All the above-mentioned steps involve working with data.
Who can Become a Data Scientist?
Data Science is a vast field that requires a bunch of skillsets that you need to master, but the process of learning data science can be broken down into small easy steps and can be learned easily by anyone who is willing to practice it consistently.
Do I need a Degree to Become a Data Scientist?
A degree in Computer Science, Mathematics, Statistics, and Business is recommended yet not necessary. If you don’t have any of the mentioned degrees. You can pursue any good Data Science Course. It will take you a minimum of one year to cover all the topics. It is recommended to start an internship simultaneously and do as many projects as possible.
If you specifically want to be in one of the Analytics, NLP, or Computer Vision field. You can focus more on that part while preparing.
Can a Non-Technical Person Become a Data Scientist?
Programming required for data science is very minimal and can be easily learned. Hence it is not programming but the curiosity to solve different problems and passion that can make you a Data Scientist.
Key Skills Required to Become a Data Scientist
- Programming Language (Python/R)
- Statistics and Probability
- Machine Learning Algorithms
- Excel
- Data Visualization
- Database Management
- Model Deployment
- Communication Skills
- Deep Learning
- Interfacing Tools
If you’re not sure what data science subjects you should start learning, consider the following list of topics.
1. Programming Language (Python/R)
Python
- Python is the best programming language you can learn for Data Science.
- It has many libraries that support data processing like pandas, NumPy, and machine learning like scikit-learn.
- Database integration is easy.
- There are many Python web frameworks like Django, and Flask, that can be used for creating to integrate machine learning models with web applications.
How much Python is Required for Data Science?
This is the most commonly asked question, especially by candidates who are from a non-programming background. Before starting with Python, keep in mind this is the easiest programming language you can learn, So don’t be afraid.
You don’t need to learn everything in Python as it is very vast. You can learn Python required for Data Science which includes basic logic building (loops, if-else statements, functions, etc). Learning Pandas, NumPy, and Matplotlib libraries are a must.
R
- R is a programming language majorly used in statistical analysis.
- It is a powerful tool for visualizing data in the form of graphs.
R mainly focuses on the statistical part of a project while Python is flexible in its usage and data analysis tasks. Hence, it is recommended to go with Python.
2. Statistics and Probability
You can never become a good Data Scientist if you don’t know Statistics. Statistics is the core of Data Science or any field that deals with data. Statistics helps you to understand data, analyze data, get insights from it, and make inferences.
How much Statistics is required for Data Science?
Statistics is a very diverse field. The major topics you need to learn are Descriptive Statistics, Inferential Statistics, and Hypothesis Testing.
It is recommended to learn Statistics before starting with Machine Learning.
3. Machine Learning Algorithms
Machine learning involves theory (mathematics behind ML Algorithm) and practical (applying ML Algorithm to the problem using some library). The theory part is equally important as it helps you to understand which algorithm can perform better, how the model can be optimized, and how to evaluate the performance of the model build.
4. Data Visualization
This is an underestimated skill but is the most vital one. Data Visualization is the process of representing your data in graphical format, understanding the inference from it, and explaining it to others.
You should learn the different plots available, the purpose of each plot, and how to get insights from each plot.
Matplotlib and Seaborn are Data Visualization libraries in Python. In Industry projects, Data Visualisation tools like Tableau, Power BI, and Microsoft Excel are widely used.
5. Excel
A Data Scientist is working with excel most of the time hence it is required to be fluent in excel. If you know different shortcuts, it can save you so much time.
Excel has many limitations but still, it is widely used across industries.
6. Database Management
You can never skip databases. With growing data, NoSQL databases that can store unstructured data are also available along with SQL databases. If you know basic SQL queries, it is easy to switch to any database.
7. Cloud Computing
In real cases, Machine Learning projects are never done on a local system. Instead, cloud technologies like AWS(Amazon Web Services), Azure (Microsoft), Google Cloud, and IBM Cloud are used. It is good to have certification in these technologies yet not compulsory.
You can become an MLOps Engineer if you have a very good understanding of these Cloud technologies.
The Machine Learning project lifecycle also follows the Agile process which involves CI/CD pipelines. Hence it is good to have knowledge of Git Commands.
8. Communication Skills
Data Scientist needs to regularly connect with the client to discuss the business problem, domain expertise to understand about the domain in detail, brainstorm with the team, data engineers for data, MLops team for deployment, etc.
There is a lot of Communication required in the daily work of a Data Scientist. Hence it is required to explain your work efficiently. It can also involve Structural Thinking, good Presentation Skills, good Dashboarding Skills, Storytelling Skills, etc.
9. Deep Learning
Deep Learning involves Neural Networks, Natural Language Processing, and Computer Vision. Learning basic neural networks which include forward and backward propagation, different loss functions, etc. is a must. You can choose NLP or Computer Vision based on if you are interested in working with text or image respectively.
10. Interfacing Tools
It is optional but good to have skill. You may want to present your project using an interface after creating a model. You can learn simple web framework tools like Flask and FastAPI (both are based on Python).
Advanced Topics
Depending on the domain you are working in you may need to learn topics like Time Series, Churn Prediction, Risk Modelling, Customer Analytics, etc. These are advanced topics required for a specific domain.
Data Science 365 Course is good to learn these topics.
Conclusion
Mastering all the above skills requires time and experience, you can’t become a master of all in small time. So don’t keep unrealistic hopes. It is recommended to start learning these skills, do projects, participate in competitions, attend workshops, and most important try to get an internship or job and continue learning.
Data Science is still an emerging field, so everything new comes up regularly. Keeping yourself updated and continuous learning is required in this field which forces you to think about whether this field is for you or not. Never learn data science just because it makes good money. Everything you are interested in can make money, you just have to figure out ways.
Data Science has wide applications in almost all domains which allows you to choose the domain of your choice like finance, retail, healthcare, education, etc., and be a data scientist in that domain. But it is recommended to explore different domains that will help you to know your interest better.
Feel free to ask any query or give your feedback in the comment box below.
Happy Learning !!
