Data scientist are eternal to the success of any organisations in this current data-driven world, where there is still a colossal amount of data to be analysed. Today, every company strives to acquire professionals who are proficient in data science. However, becoming a data scientist comes with its fair share of challenges.

Difficulties In The Path To Becoming A Data Scientist

Firstly, there are very few colleges that offer data science course, thus there is a huge demand and supply gap in the current market. Secondly, the course fee is too high, thereby making it unaffordable to many aspirants.

Data science aspirants are opting for paid online courses for streamlining their learning to enhance their skills. However, if you are willing to gain the skills of a data scientist, you can do it for free as well. The only tradeoff is you will not be certified by any institutes.

Skills To Learn

To become a data scientist, one needs to be familiar with a wide range of topics and tools while avoiding burnouts. It often takes at least 6 months to learn and expertise the skill required to become a data scientist. This can be daunting for many, thus it is paramount to devise a roadmap for minimising the burnouts.

Here are the most in-demand skills for a data scientist:

1. Programming
   1. SQL
   2. Python
   3. R
2. Mathematics
   1. Linear Algebra
   2. Calculus
3. Probability and Statistics
   1. Basic and advanced probability
   2. Descriptive and Inferential statistics
   3. A/B Testing
4. Data Analytics Skills
   1. Data wrangling
   2. Visualization
   3. Technical writing
5. Machine Learning
   1. Supervised learning
   2. Unsupervised learning
   3. Reinforcement learning
6. Other Data Science Tools

Links To Free Data Science Courses

To lay the cornerstone of your data science career, you need to be proficient in programming for querying data from databases and other sources such as web pages, and document, and then, perform analysis to unveil insights into data.

To begin, you can start by taking these free courses:

1. Intro to SQL for data science
2. Learn Python for scratch
3. R Programming
4. Intro to data analysis

It’s not mandatory to learn both Python and R, thus you can choose any one of them and later learn the other.

A tech background aspirant will already be familiar with linear algebra and calculus, so brushing up from their college books would be adequate for going forward to probability and statistics.

While people usually learn probability and descriptive statistics in school, they will have to focus on inferential statistics to achieve data intuition and infer from a plethora of data. Besides, A/B Testing learning will help you in making decisions by choosing between various approaches.

Binge learn with these statistics courses:

1. Intro to descriptive statistics
2. Inferential statistics
3. A/B testing

All the above courses will set the base to now get your hands-on analysis. This is where one has to bring all the leaning together for analysing big data.

1. Data science: Wrangling
2. Data visualization with Python
3. Free training videos – Tableau

These courses will assist in implementing programming skills to gather, clean, and visualise data. While it can get you started, it is on you to practice and become an expert, as data differs widely. Consequently, learning different methodologies to collect and clean data will help you stand out.

If you make it till here, you can call yourself a data analyst. Pat yourself on the back and move forward towards achieving machine learning skills.

Machine learning is a huge topic to learn in a short period, but the below courses will empower you with the basic skills that can be utilized over a certain period to master the skill. One needs to regularly practice efficient ways of training models and predicting future events while trying to find the balance between models’ bias-variance.

1. Intro to machine learning
2. Reinforcement learning AWS DeepRacer (Getting started)

Done with all the above? If yes, you have leapt and become a machine learning engineer. However, there is one final stride to make for becoming a data scientist.

Numerous tools make data scientists task easier and help them in storytelling with insights. Learning the below tools will enable you to learn advanced visualization techniques and manage big data effectively.

Other tools’ courses

1. Spark
2. Data visualisation and D3.js

Outlook

While we have covered the most important tools in data science, others are usually embraced in a few organizations. Therefore, learning them will be a plus as it will differentiate you from others. Learning the aforementioned technologies is just a drop in the ocean. One has to practice and develop their data intuition skills by practising and competing on several data science hosting platforms such as Kaggle, DrivenData, and others.