Data science is the most in-demand skill for which almost every professional is preparing. Following the right data science learning path is indeed an imperative task for success. The best kind of learning method is the practical way of learning, which is why it plays a big role in succeeding. Data science learning path is the right approach one may consider to get a depth-knowledge of data.
This blog covers a comprehensive guide to the data science learning path. It is not an easy tread to learn everything about data science. It is a vast field and requires one to be agile and always keen to learn. However, with these steps, it is possible to get a kickstart to one's career as a data scientist. Let's discuss the data science learning path in this article.
Explore igmGuru's Python certification course program to become data experts.
Getting an answer to 'what is Data Science' is definitely a need to understand to move forward.
Data science could be taken as the field, where people, having loads of data, make it a sure need to get the right things on the plate. This is especially true when it comes to the data and the variety of information.
A data scientist is a professional who holds an industry-recognized position. They hold in-depth industry knowledge, high level skills and analytics experience. They extract insights from unstructured and structured data with potential business impact.
They are high-ranking team leaders with good positions in analytics organizations. Their demand is growing since most functions and industries are embracing analytics. Hence, having a data scientist on the team is becoming more of a necessity.
There are plenty of reasons as to why someone would choose a career in data science. Here are a few of those.
With a lot of information coming your way, this helps in making sure that you are occupied and taking things in the right way. This also helps in bringing new knowledge to your mind and keeping you safe from every kind of boredom and thus, happening and asking things possible every single time.
Many greater opportunities can come rolled in when it is about making sure that every new opportunity is about learning new things. The job of a data scientist is one that brings along unlimited new opportunities for success in the career.
Never underestimate what your one area of interest can bring in for you. Data science has become an integral aspect of many organizations across various industries. This brings us to the point where it is feasible for professionals to experience growth with data science in different industries.
Related Article- Your Guide To Data Science Careers
Data science learning path includes various steps. There are plenty of things to learn to become a professional data science expert. These are the must-learns to be successful.
While many people advocate picking learning a programming language as the first step, learning SQL is equally important. It is popularly used by organizations everywhere as a language to analyze and manipulate data. A lot of companies just expect the professional to have knowledge and skills in using SQL. It has, in fact, become a basic requirement for many data science roles.
There are many programming languages out there. Amid these, Python and R are chosen most often for data science. The first thing one should do is get comfortable with Python. It is the world's leading language and is used majorly for data science purposes.
It is beginner-friendly and is known to be very good for freshers. It has a simple syntax and is pretty easy to code. One must also begin by learning about its various frameworks and libraries. These make it such a great language.
After learning to code in Python, it is time to commence performing data analysis on different data sets using this language. There are many things to perfect here, including Numpy, Pandas and dataframes. Importing and visualizing data with libraries like Seaborn and Matplotlib will also help.
Once this is done with, next comes machine learning. It is important to know about libraries that help in building Machine Learning models, such as Scikit-Learn. Some of its key concepts should be mastered. These include supervised and unsupervised learning methods, K-Nearest Neighbors, Linear Regression, etc. Machine Learning algorithms are a big part of becoming a data scientist.
Everyone knows that becoming a data science professional is all about gaining the right knowledge. This can be done flawlessly by enrolling in a leading data science training course. An online program is highly in-demand today because of the various benefits attached to it.
Since the field of data science is always changing, it's imperative that a data scientist always keeps on learning and practicing. One must never stop by thinking that they have all the necessary knowledge. In fact, keeping oneself updated is the best way to stay an asset for an organization.
Becoming a data science professional is not an easy feat. However, it is also not impossible and this learning path certainly helps all aspirants. There are many things one has to learn to become qualified to be called a data scientist. These skills and steps are just the basic ones. It's a never-ending learning journey.
Course Schedule
| Course Name | Batch Type | Details |
| Data Science Courses | Every Weekday | View Details |
| Data Science Courses | Every Weekend | View Details |
