What Are The Learning Tricks Every Data Science Binigers Should Follow?

7 Simple Tips for Data Science beginners in 2021

By Trisha Manna in Learnbay

May 14, 2021.

 

Initiating the learning path for a data science career transition is not a hard job. The hardest task is identifying the right learning trajectory and following the most crucial measures at every pinpoint of your learning journey.

 

For a successful career switch, you need to make a smart plan learning plan rather than a time consuming and hardworking, conventional plan. Below are the seven learning tips that will help you evaluate a smarter and successful route map to your first data science job.

Tip#1: Don’t ignore revising your high-school grade mathematical skills

Probability statistics and machine learning algorithms are the key statistical approaches used widely across all entry-level data science job roles. But jumping directly to a trending algorithm will be nothing but a shortcut. These may lead to a massive skill gap within you, making your job security questionable.

 

The better way is to start revising your  10+2 level mathematics, such as,

●      Matrix calculation

●     Basics of coordinate geometry

●     Linear algebra

●     Probability theory

●     Differential and integral calculus

Slowly move forward to the complex applied mathematics modules like,

●     Linear regression

●     Logistic regression

●     Multivariable calculus

Tip#2: Never dare to ignore data science books

We all have become a virtual entity. For any information related to head-to-toe, we start typing on google.

 

Online courses are now the most valuable learning option, mainly for working professional. But the benefits offered by books can't be replaced by such online sources. Books offer you the best opportunity for in-depth and sequential learning on a particular topic.

 

So, apart from streaming online videos and scrolling for articles, make a practice reading different data science books. To know about relevant books, read the blog: Top 7 Books Every Data Science Aspirant should Read in 2021[1] .

 

Tip#3: Switching domain is not a good idea (for working professionals)

This point is especially for working professionals seeking a career transition to the data science field. Under such a scenario, the recruiter first search for the higher proficiency of domain expertise.

For example, if you are in a marketing domain, as a data scientist, your demand will be as a marketing data analyst. However, targeting a data scientist role in a manufacturing role will be a real hurdle for you, and the chances are high that your career shifting effort become a failure.

 

So, always stick to the domain that has worked in or has academic knowledge.

Tip#4: Sprinkle the splashes of uniqueness to your critical thinking and analytical skill

Critical thinking and analytical proficiency are the too key skill that every data science needs to acquire. But to stay out of the huge data science applicant's crowd, you need to takes these skill to the next level. Try to think and analyze every business problem as well as real-life problem in a different way. The best way is to target trendy, innovative, but future-ready analytical output for all of your data science problem.

Tip#5: Focus more on data presentation

All the hardship you invest in data analysis, monitoring, data collection, and cleaning remain hidden in the background and fades away over time. Instead, what gets highlighted and credited is your visual output.

 

Yes, the success stories of data scientists depend on their data presentation ability. So from the beginning, you should concentrate more on data visualization aspects. Learn the following tools.

●     Tableau

●     Seaborn

●     TensorFlow

Tip#6: Keep growing through continued learning

 

Not a single career is sustainable until you keep learning. However, data science comes with ample scopes of future growth.

Learn from your working experience, keep heading towards more complex branches of data science, like AI, ML, etc.  In case you use a non-programmer, first, learn the AI-powered programming tools. Then, once you get a job, continue learning core programming.

Stay updated with the upgrades of the tools, strategies, and application you are currently working on. You can do that in the following way.

 

●     Streaming tech videos

●     Playing tools related podcasts

●     Reading AI and ML articles blogs.

 

You can visit Medium/learnbay-blogs for such articles.

 

The Master Tip

Always choose the proper guidance. Apart from your MOOC course, you can join the cost-effective data science and machine learning courses by Learnbay., Where you will get the following unique benefits:

●     Live online theoretical session.

●     Offline hands-on industrial project with domain-specific startups.

●     Placement assistance and Mock Interview support.

To know more about Learbay data science courses, visit www.learnbay.co.

 

 

 

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link the blog once get published. already submitted.