How Long Does it Take To Learn Data Science From Scratch

With the growing demand, hot salaries, and promising career opportunities. It is high time for you to be in the field of data analytics. Data Analyst, Data Engineer, and Data Scientist are considered to be the “big three” job profiles in the data analytics domain.

“Data Science”, this term attracts the mind of so many job seekers but still is regarded by many as very hard or even impossible to learn from scratch. There are so many myths about this field. In this article, we will learn how difficult data science is, how much time it takes to learn data science from scratch, and how you can get a job in data science.

Is Data Science Hard to Learn?

There is nothing difficult if you are passionate about it. The first step is to understand where you stand that is, whether you have to start from scratch or you already have a few required skills. The skills required for data science jobs can be divided into three levels: basic, intermediate, and advanced. You can start from the basic skills which are very easy to learn and then move to more advanced ones. Let’s see what skills are needed to be a data scientist and the amount of time they consume to learn.

Basic Level Skills (5-7 months)

A few basic data science skills are as follows:

  1. Python for Data Science (1-2 months)
    Python is a programming language that can be used for a variety of tasks like web development, game development, automation, and of course data science. You can only focus only on topics required for data science and skip the rest of others to save your time and effort.
  2. Pandas, NumPy, Matplotlib ( 10-20 days)
    Pandas, NumPy, and Maptplotlib are three python libraries that are widely used. Hence having good hands on them is essential.
  3. SQL (1-2 months)
    Databases cannot be skipped in any profession that involves software and coding. Data Analysis involves a wide variety of data, some of which is unstructured for which NoSQL Databases are used. Hence you should have a good understanding of SQL queries and should be capable of switching to different databases as per the requirement.
  4. Probability and Statistics (3-5 months)
    Probability and Statistics are the roots of the whole data science field. They help you understand the data in depth and extract inferences from it. Learning statistics may take time for some of you, but it is worth the time spent.
  5. Data Cleaning (1.5-3 months)
    Mostly the raw data available for any project cant be directly used. Some preprocessing is required to convert raw data into a format to which machine learning algorithms can be applied. Data cleaning is a process used for this task.

Intermediate Level Skills (8-12 months)

A few intermediate data science skills are as follows:

  1. Machine Learning Algorithms (5-6 months)
    ML Algorithm requires a good amount of time to master.  You need to have both theoretical as well as practical experience working on different kinds of algorithms (supervised and unsupervised). Start with regression, then move to classification and clustering algorithms. Working on sample projects using machine learning algorithms is a recommended way to learn them fast.
  2. Hyperparameter Tuning (2-3 months)
    Hyperparameter tuning is a process of finding the best parameters of a model that can fit your data well and returns good prediction results. It requires a fair understanding of ML algorithms. Pick a few problems from Kaggle and start working on them using the ml algorithm and tune the model until you reach a good accuracy.
  3. Clustering (1-2 months)
    Clustering is popular for its wide range of applications. You can learn different unsupervised machine-learning algorithms which help implement clustering. You can even pick a real-life project to understand it better.
  4. Data Visualization Tools (1-2 months)
    Data Visualisation is one of the must-have skills in the data science domain as it helps you get insights from data and take business decisions based on that. Various tools like Tableau and Power BI can be used for visualization.
  5. Excel (2-3 months)
    Excel is the oldest tool used for data processing which is still in huge demand. Having good hands-on excel can make your daily work very smooth as a data scientist.

Advanced Level Skills (14 – 20 months)

A few advanced data science skills are as follows:

  1. Deep Learning (12-4 months)
    Deep Learning is an advanced and comparatively difficult skill. But the demand and applications of deep learning give you a good reason to master it. Getting a job in this domain becomes very easy if you have this skill in your resume.
  2. Natural language processing  or Computer Vision (10-14 months)
    NLP and CV are two areas that are based on Deep Learning. NLP is used for text processing and CV is used for image processing. You can pick one of them based on your interest. Learning both can help you work on a wide variety of projects.
  3. ML Pipelines (4-5 months)
    Machine learning pipelines help you join all the subtasks of a machine learning project in an order such that output from one becomes the input for another. Data and model quality can be checked at each stage.
  4. Cloud Technologies (6-8 months)
    If you have experience working with cloud technologies like AWS, Azure, or GCP. It can increase your demand so much in the market. This is good to have skills but not compulsory.

Learning Tips for Data Science Self-Study

  1. Just Start
    Many a time we don’t even start to work for our dream job just because we assume it is difficult. But the demand for data analytics roles gives you a reason to at least give it a try. Just take your first step and try to find out if you are really interested in this domain.
  2. Be Consistent
    If you have made up your mind to go with this field, just be consistent and don’t stop. Consistency is the key to achieving anything.
  3. Don’t Skip Theoretical Concepts
    I can’t give enough focus on the fact that theoretical knowledge of different concepts in statistics and machine learning is equally important.
  4. Focus on Projects
    Working on projects gives you practical experience and increases your chance of getting a job call. At least one project is a must-have if you want to get an interview call.
  5. Understand your interest
    There are various paths you can follow to be a part of the data analytics industry like Data Analyst, Data Engineering, Data Science, etc. You can choose among them based on your skill set and interest.
  6. Connect with people who are already in this field
    Connecting with people who are already in this field can help you in a lot of ways. You can get references from them for the jobs, ask them about the path they followed to reach this position, new updates, and requirements in the market, and also know about their daily work life.

Can You Learn Data Science on Your Own?

Definitely, you can learn data science on your own by following a step-wise process and consistent efforts. The complete roadmap for learning data science is already covered in this article.

It is easy to learn on your own if you have a background in computer science, mathematics, or statistics. But if you are from a completely different background that is nowhere related to analytics or programming, doing a course will be a better option.

Enrolling in a course has its own advantages as all the topics are covered in order and psychologically you become more conscious about learning a topic if you are doing a course. Some of these courses also help in placements.

How to Get a Job in Data Science

If you have the required skills, you can start applying on different job portals. Don’t wait to cover all the topics, start applying if you are at the intermediate level. You should always prepare for interviews in advance: Understand the flow of the interview, collect information about the company, etc. I have already covered How to Prepare for Data Science Interview: Important Topics and Top Tips in the previous article.


It will take you at least a year to cover the complete data science course. Data Science job is in great demand and hype but there are other roles also in the analytics field which are comparatively easy to get like Data Analyst or MLOps Engineer. You can read more about these roles from Complete Roadmap to Become a Data Analyst in 2022- Career Guide and Skills Required to Become MLOps Engineer.

Thank you for reading this article till the end. I will appreciate your feedback in the comment box below.

Happy Learning!