Over the last decade, data science has been witnessing glorious and rapid growth, with several businesses searching for ways to use this concept for numerous operations. As a result, there is a sudden increase in the demand for data scientists, influencing people either to sharpen their existing skills or to change their career altogether to make something out of this field.
As of 2021, the market size for analytics has surged to $45.4 billion, experiencing a whopping growth of 26.5% in comparison to 2020. Not just that, it is also expected to rise at a CAGR of 26.9% until 2027.
Furthermore, as the investment for research and development increases, there is a rapid technical advancement occurring across the market. Thus, when it comes to learning data science, you surely would not face a dearth of resources.
While the available resources help professionals to a great extent, it is easier for noobs to feel lost and confused. If you are standing in the same place, this post will offer a step-by-step roadmap to learn data science in the best possible way.
Table of Contents:-
- 1 A Detailed Understanding of Data Science
- 2 Roadmap for Learning Data Science
- 3 Step 1: Get the Required Fundamental Technical Skills
- 4 Step 2: Loving the Data
- 5 Step 3: Practicing the Knowledge of Data Science
- 6 Step 4: Learning How to Communicate the Insights
- 7 Step 5: Learning from The Peers
- 8 Step 6: Increasing the Difficulty Level Constantly
- 9 Step 7: Added Skills Required to Be a Good Data Scientist
- 10 Conclusion
A Detailed Understanding of Data Science
Data science refers to the field that utilizes data for solving problems and bringing value, impact, and insights to various organizations and companies. So far, this technological field has managed to benefit a broader range of disciplines and industries, including finance, education, healthcare, geology, e-sports, travel, etc.
However, to begin a full-proof career and to become a skilled data scientist, it is essential to have the following technical skills:
- Data collection
- Pre-processing of Data
- Data Analysis
- Statistical Analysis
- Visualizing data
- Machine learning
- Software engineering
- Programming Skills
Apart from these technical skills, being a data scientist also demands a handful of soft skills, such as:
- Business acumen
- Communication, etc.
Roadmap for Learning Data Science
Now that you have made up your mind to enter this complex yet equally exciting field, refer to the below-mentioned step-by-step guide to frame an adequately fruitful career in data science.
- Get the Required Fundamental Technical Skills
- Loving the Data
- Practising the Knowledge of Data Science
- Learning How to Communicate the Insights
- Learning from The Peers
- Increasing the Difficulty Level Constantly
- Added Skills Required to Be a Good Data Scientist
Now we will know more about them in detail:
Step 1: Get the Required Fundamental Technical Skills
Not just with data science, learning the fundamental, necessary skills for any career should be the first step to implement. As far as a data science course is concerned, here is what you would need to choose the best one:
- A Programming Language:
Two popular languages to start learning data science are Python and R. There are multiple libraries and frameworks present for both to simplify the essential tasks of a data scientist.
R programming focuses more on statistical computing, whereas Python has a broader scope. Thus, learning Python is more recommended as it can be used in other areas as well, including web development.
- All About Statistics:
This is also referred to as a building block of data science. Without a decent understanding of statistics, you might face a lot of difficulties in data analysis and interpretation. Statistics are necessary to explain the data and infer results about the provided data set.
- Linear Algebra:
Data gets collected in various formats, including images, texts, sound waves, etc. But for performing analysis, it must be present in the form of numbers. It is not sufficient to just convert data to scalars. This is because, with the increased amount of data, operations on scalars automatically become inefficient. That is where the role of linear algebra comes into the picture to enhance computational efficiency.
- Machine Learning and Deep Learning:
Machine learning and deep learning are two varying subfields of data science that allow computers to learn from the provided data without explicitly being programmed. Also, they make you understand the designing of algorithms based on neural networks. Thus, you must have a brief understanding of both algorithms to perform data science operations efficiently.
Step 2: Loving the Data
As they say, excess of everything is bad. However, if you start loving the excess, you may change the game altogether. One of the most critical steps in the journeys of becoming a data scientist is to love the data sets.
Data science is a broader field, and thus it requires more time and patience to understand all the concepts. And with a lack of motivation, it gets even more difficult. You must have the motivation to continue with the learning process even at midnight when the formula and concepts might start looking blurrier.
This motivation is not required in the form of inspiring quotes and speeches, but it must be there as your passion project to help drive your learning. Being adept with statistics and probability comes next. Still, the priority is to get the best online data science course and start a project to experiment with the concepts and start writing the codes to predict various changes in any market or analyze the data set.
As you continue working, you will start to love the bigger data sets and get motivated towards learning more and making better programs. It would be best if you find the things that make you want to learn. And this is a lot easier as data science has infinite exciting things to work on.
Step 3: Practicing the Knowledge of Data Science
When you begin to learn data science, apart from having brief knowledge of the technical skills, you must:
- Know how to clean the provided data set
- Practice the learnt algorithms which you learn
- Keep the quality in mind while keeping the quantity aside
- Know the libraries to implement various algorithms and their operations
While working on a project during the learning process, you get to practice the learned concepts to sharpen the expertise. Projects will be helpful to let you acquire the skills and immediately apply them for managing the data set whenever required.
The most preferred way to find the best data science project is to discover the type of data sets that holds your utmost attention.
Step 4: Learning How to Communicate the Insights
Being a data scientist, you will have to present your analysis results to others. As a part of communicating insights, you must understand the topic and theory well. Additionally, it also involves understanding the concept of organizing the results.
Therefore, you must be well aware of how to do this efficiently. Data analysis is mainly valuable in a business if you can convince people of your organization to act over your findings, meaning learning to communicate data.
Step 5: Learning from The Peers
You can learn a lot while working with others. When it comes to data science, teamwork plays a vital role in a job. Data scientists often work as a team to solve various problems. These professionals work from team to team while answering data questions for numerous parts of a company or organization.
Therefore, being able to collaborate is more essential for data scientists than anyone else. Here is what you can follow for the same:
- Find people to collaborate with at the meetups
- Contribute to open-source projects and packages
- Connect with people who write exciting data analysis blogs
Step 6: Increasing the Difficulty Level Constantly
Are you comfortable with the project you are working on and well-versed with all the essential concepts? If yes, well, it is time for you to increase the difficulty level of the project.
Constantly increasing the level is essential for continuing the learning process and reaching out to the top. Here are the ideas to help you increase the complexity and difficulty level of your data science project.
You can choose any one or a combination of some to get yourself out of the comfort zone:
- Increase the size of the data set often
- Try to increase the speed and efficiency of the chosen algorithm
- Try scaling the algorithm for multiple processors
- Precisely understand the theory of the chosen algorithm
With this practice, not just you will get to learn something new with each passing day but also get a deeper understanding of the topic than before. In the end, you will have better expertise and boosted confidence.
Step 7: Added Skills Required to Be a Good Data Scientist
There are various additional abilities required to be a professional data scientist. Apart from the essential technical skills as mentioned priorly, there are a few more that you must add to your bucket list, such as:
- SQL and query writing
- Collaboration and workflow knowledge
- Information regarding git, command line, etc.
Adding these fundamentals to your skillset will surely help you land the most desired data scientist job.
Following the right steps to perform any task or learn any new technology effectively gets the right outcomes. As for learning data science, the roadmap mentioned above is perfect for helping you lay the initial stepping stones and move further while learning more and better.