a strategy for successful data science career-skillslash

A guide To smooth Data science Career transition by Skillslash

Data science is now tossing the entire world on its finger. No such domain currently exists that could safeguard itself from the paw of emerging artificial intelligence (AI) and machine learning (ML) technology (the most dominating sub-domains of data science). Everybody is dying for a successful data science career transition.

The rise of AI and ML, already dropped the staffing requirements in so many job roles that used to be the top demanded once. Sooner everything will be under the roof of data science innovations.

The realization of the above hard truth has made everyone crazy about stepping into the field of data science domain anyway. So many have already been succeeded in achieving a sustainable transition while thousands of aspirants failed to do the same.

If you don’t want to be such a loser, then read this blog very carefully. This will help you throughout your data science career transition journey.

What is the right time to switch your career toward the data science domain?

This question owns the scopes of two different answers from two variable perspectives

  • Year of working experience and
  • Data science job market scenario

Let’s first consider the first perspective: The working experience of a professional.

Well, here the answer becomes there is no right time or wrong time for taking your career forward to data science opportunities. But yes, it’s always easier to transition your career to a new domain at a very early stage. However, that does not mean you will make a mistake in case to do the same after working for 4 years in any domain. Even with 7+ years of working experience, you can plan for this career switch.

The most important thing is that whatever be the number of your working experience year, you need to take the decision of career transition at the right time. And the right time is when your target career demand starts experiencing a notable hike and insights for a massive surge on the same in upcoming years.

From these aspects, this is the right time to switch over for you.

Second aspect of career transition: Data science job market scenario

If you explore the current trends of the data science job market, you will find that even SMEs are not thinking twice about investing 4 to 5 lacs/ annum on a data scientist. Freshers candidates (in data science) of different domains (with 1 to 2 years of domain experience) with real-time project experience are the key target of such companies. At the same time, domain experts with 4 to 5 years of experience and industrial data science project experience become the target for MNCs.

However, the scenario will change within the next few years.

At present, data science is comparatively a new innovation within the global as well as Indian job market. So, this domain is experiencing a massive skill shortage, hence recruiting quite randomly with the aim of training promising talents.

With the advancement of time, the field of data science will approach the state of skill saturation for entry to mid-level talents, and only there will be the need for an expert mind.

From the above future insight, you can easily track how much tougher the data science career shift will be after a few years. Hence, again the answer becomes, this is the best time to roll over a data science career switch.

7-Steps toward A smoother Data Science Career Switch From Any Domain

As mentioned in the beginning, every domain is now dependent on data science, so no matter from which domain you belong, you own ample scopes of securing a promising data science role. To land in a data science job role with the vast possibility of career growth will not be a hard task, in case you follow the below steps.

1. Learn to love statistics

Data science was born from statistics. The Combination of foundational statistics-based data analysis and computer science gave birth to the more advanced sub-branches like artificial intelligence, Machine learning, and deep learning. So, for a successful entry into a data science career, you have no other option than to love statistics.

As a bigger, try to point out all the trending and most demanded concepts and chapters of statistics from data science aspects and learn those from the core. Invest your learning time on the following.

  • Linear algebra
  • Theories and axioms of probability
  • Correlation calculus
  • Coordinate geometry of lines and curves (parabola, hyperbola, etc.)
  • Several algorithmic approaches like linear regression, logistic regression, SVM, etc. (Machine learning).

The best way to make statistical learning more interesting is to try to solve everyday life problems and your own working domain-related issues with simple statistical analysis.

To know more about ML algorithm, read the blog: Know The 8 Most Demanding Machine Learning Algorithms of 2021–22 | Skillslash

2. Explore the magic of programming

Programming is real fun. Although some feel programming tasks monotonous, when you have to apply statistical knowledge in programming, then it doesn’t remain monotonous anymore. As the first step of data science programming concentrate on the two most widely used programming languages: Python and R.

But do you need to learn core coding? If you are an IT or software professional, I would suggest enriching your coding knowledge to ensure a machine learning expert or AI engineer as your very first data science job role.

But for others (non-programming background), it’s enough to learn the fundamentals of coding like strings, commands, etc. But such aspirants need to dedicate more time to exploring and mastering the programming libraries and tools like

  • Pandas
  • NumPy
  • Tableau
  • SciPy
  • Scikit-learn
  • Stats (for R)
  • Plotly

3. Be able to play with data modelling

This is a critical bent within your learning curve. The learning module of data modelling completely depends on your domain knowledge. This step includes exploring, analyzing, and experimenting with data models that are relevant to your own domain.

Through data modelling, you need to draw the inter-relational diagram among the tons of data that has to be stored in your organization’s database. Depending on your domain and the types of business issues, you need to evaluate the right data models that offer the best possible scopes of data-driven insight generation. Below are some tools that you need to learn for effective data modelling.

  • My SQL Workbench
  • Draw.io
  • Lucidchart
  • Erwin data modeller

4. Acquire knowledge of database

Although there are now specific professionals for managing databases, to work with data, you need a foundational understanding of databases. At the initial stage of your data science job roles, recruiter demands a sound knowledge of most widely used DBMS (database management systems) applications like,

  • MySQL
  • Amazon Athena
  • Microsoft SQL server
  • MongoDB

5. Waken up your inner researcher for data visualization

Your data visualization ability decides the nature of your data science career growth curve. In the data science domain, your knowledge and effort is not acceptable until you provide your analytical output via adequately insightful graphs and charts. The data visualization proficiency demand of data science is not going to satisfy with advanced excel of SPPS. Rather you need to make yourself comfortable working with the following data visualization applications.

  • Tableau
  • Infogram
  • Sisense
  • DOMO
  • IBM Congo

For data visualization, you need to keep in mind that your graphs and charts must have the capability of solving the maximums possible and all of the probable data quarries of peer groups.

6. Earn the most popular badge for communication

A data scientist has no worth until your communication skills become a valuable asset for the organization.

Yes, before applying for a data scientist role, make sure that you are an expert in clear and crisp communication across a huge audience. Because as a data scientist, you need to communicate with all the existing as well as upcoming working divisions of your organization.

Besides, you need the eye-catchy convincing ability to satisfy your audience with your analyzed results and insights.

7. Choose the right course that suits all your career switching needs.

Last is about the data science course. While discouraging self-paced learning is acceptable. Still, the truth is, you need structured guidance throughout your data science learning journey. Although to ensure that your learning journey is up to the mark with regularly changing market demands and latest trends, it’s always suggested you enrol for a highly creditable data science course.

You can join the Skillslash certification program on AI and Machine learning, specifically designed for working professionals and planning for a successful career switch. To know more about course features, career fees, benefits, visit www.skillslash.com.

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