Traditionally, we have all been used to learning things from books that gave us a detailed idea about any topic step-by-step. Fast forward to today, people have started exploring resources available over the internet to learn about any new topic. Working professionals, especially, do not have time to enroll in an academic course and often look for eLearning platforms to gain in-demand digital skills. One of the trending skills that has gained the attention of technology pros across the world is data science. A number of individuals have realized that data science is a promising career option and learning it would help them reach new heights.
There are many reliable online training providers that offer comprehensive data science courses and help you learn everything from scratch. All you would require to access the course material is a computer system connected to the internet. For example, if a person is seeking a data science course in Delhi, he can enroll in any course available worldwide through eLearning platforms. The best part is one can get guidance from the world’s best instructors at the comfort of their home.
As mentioned in the beginning, learners are used to going through any topic step-by-step, one needs to ensure that the online course they are seeking guides them the same way. However, people who are just starting to learn data science might not be aware of the topics that a course must cover. So, this article lets you know how you can learn data science from scratch and how to choose the right data science course.
The Basics of Data Science
Data science is a term used to describe all the steps involved in extracting meaningful information from raw data so as to support business decision-making, ensuring better customer service, strategic planning, and more. It is an umbrella term encompassing many areas like data analytics, business intelligence, machine learning, data engineering, and data visualization. The reason why data science has garnered so much attention is that we are generating a massive amount of data these days and companies now have the computational power to process such a huge amount of information and gain actionable insights.
So, if you are ready to learn data science, here are the topics you can start with.
Know about the data science process
A lot of steps are involved right from gathering raw data to finally gaining valuable insights. Data collection, data cleaning, data analysis, data modeling, data visualization are the various steps involved in the data science lifecycle. So, you need to be familiar with how to identify a business problem, gather relevant data and clean it, try out different analytical methods, decide the most suitable model and deploy it, and visualize the results so that they can be explained to business executives.
Be clear with core mathematics
A variety of core math concepts come in handy when you are working on a data science project. You should have a clear understanding of topics like linear algebra, calculus, probability, and so on. Next, statistical methods are a central part of data science and used in different machine learning algorithms. So, we recommend you have sound knowledge of descriptive statistics and inferential statistics.
Learn a programming language
Programming language basics are a must in any area of computer science and the same holds true for data science as well. Programming languages like SQL, Python, R, and Java are widely used in the entire data science lifecycle, including data wrangling and data analysis. You need to know the object-oriented programming basics and learn about its various libraries that are specifically designed for data science purposes. For example, if you are learning Python, libraries like Pandas, NumPy, SciPy, and matplotlib come in handy.
Data engineering and machine learning
The raw data collected from disparate sources isn’t ready for analysis right away. It is the task of data engineers to clean that data, deal with missing or duplicate values, and transform it into a single format so that data analysts could take it up. So data engineering includes building a data architecture, streamlining data processing, and maintaining large-scale data systems for which tools like Shell, Python, Scala, and SQL are needed. Next, the creation of data models requires knowledge of machine learning concepts and algorithms. So, you should know what algorithms come under supervised and unsupervised learning and how to apply them.
Storytelling, data science techniques, business acumen, big data tools, deep learning, and exploratory data analysis are some other important concepts to learn if you wish to start a data science career.
Selecting the Right Training Course
Now that you know what topics should be covered in a data science training program, you should begin your hunt. You can explore courses offered by reliable training providers like Simplilearn, edX, Udacity, and Coursera. You can select a specific sub-field of data science or enroll in a specialization program that covers the entire concepts of data science. These platforms also offer online courses that are designed by renowned universities like MIT, Harvard, Purdue, Imperial College of London, and give you a chance of learning from their expert faculty. Go through the syllabus covered, testimonials, instructor profile, and learning modes offered, and choose the one that best suits your career needs.