11 Steps to Transition into Data Science (for Reporting / MIS / BI Professionals)

Finding a business problem can be difficult.

You should talk to the leadership or team managers and take one of their business challenges as your project.

Here, the first step is to convert the business problem to a data problem.

Then, start moving down the steps we had discussed in point #5 earlier – hypothesis generation, data collection, data exploration, data cleaning and finally model building and validation.

One of the major advantages you have as a BI professional is that you are already familiar with the variables in the dataset.

Your detective analytics skills will help you understand the variable(s) relationships as well.

You can jump to tasks like data cleaning, transformation, identifying the right evaluation metric, setting validation set and finally model building.

You should take some time and watch the below webinar by Tavish Srivastava to understand the importance of defining the problem statement and hypothesis generation: I also recommend going through the below articles on building models easily and effectively in R and Python: A comprehensive guide to build machine learning models from scratch Build a Machine Learning Model in 10 minutes using R Build a Machine Learning Model in 10 minutes using Python Challenges and Solutions: Not able to find the business problem: You will encounter scenarios where you are not able to get a business problem or are not able to convince the business/team managers about your skill set.

If that’s where you stand, then start exploring these methods: Start building the model at your own capacity for a more defined problem statement: Let’s say you are responsible for generating a report which has agent (insurance agent) level month-on-month business sourced.

Now, while publishing this report, you can also estimate the agent performance for the upcoming months.

This will be based on demographics or past performance as you already have access to the required datasets.

After a month or so, you can validate your results and check how good your estimate was Participate in open data science competitions and improve your profile: Participating in data science competitions is a wonderful way to learn data science, improve your knowledge and profile, and gauge where you stand viz a viz the top data scientists in the world 9.

Share your model’s results with the business owners and earn their trust After building your model, you should share the results with your supervisor or the people who make decisions (like the team or project manager).

As a data science professional, it is very critical to share your findings (like which feature(s) is making an impact on the target variable).

You should also communicate regular updates around the comparison between your model result and the actual numbers.

This process will also help you to tune and improve your model.

If the model is performing well, then there is a high chance you will get another assignment or get involved with the core data science team.

That’s what we are aiming for, right?.  Challenges and Solutions: My model is not performing well, what should I do now?: It is okay if your model is not performing well.

You can explore the dataset further and look for issues.

We will focus on learning different algorithms that might be a better fit for the problem you’re solving   10.

Keep learning new algorithms, engage in the data science community and focus on profile building Learning never stops in data science.

It is an ever-evolving field and we need to keep evolving with it.

You’ve learned linear and logistic regression so far – extend your knowledge beyond that now.

Learn algorithms like decision trees, random forest, and even neural networks.

And like I mentioned before, you should learn by applying.

Theoretical knowledge is good to have but it’s useless if you don’t put it into practice.

Pick up the datasets we spoke about earlier and apply these newly learned algorithms.

You are likely to see a significant improvement in your model!.Now, let’s take a step outside the tools and techniques.

I want to emphasize on the importance of building your network and profile in the data science community.

Start attending data science focused events like meetups and conferences.

You will meet like-minded people as well as experienced professionals who can guide you.

I have seen plenty of aspiring data science professionals acquiring job offers through these events so I an vouch for their usefulness!.You should also focus on the digital aspect of your profile.

You have clearly been working with data science projects so showcase your work to the community!.Upload your code to GitHub and start publishing blogs/articles on your findings.

This helps prospective employers see that you possess good knowledge about the subject.

  Challenges and Solutions: I can’t decide which algorithms I should work on: This is a classic all-time question.

It has baffled and puzzled many data science aspirants.

My advice – work on the algorithms that are being used in your organization.

This lasers down your focus to what’s required by your data science team.

That intra-organization transition we spoke about earlier?.This is a great approach to showcase your value to the existing data science team I don’t know where to find groups or which ones to join: There are plenty of meetups happening thanks to the data science boom.

Analytics Vidhya hosts meetups regularly.

If you can’t find a meetup in your city, host one!.I have seen plenty of people take the initiative, post the meetup details on LinkedIn and Meetup.

com, and ask their network to come and join.

You’ll be surprised by the number of people who show up   11.

Focus on transitioning to a data science role within your organization While there are no easy ways to transition into data science, there are certain well-trodden paths.

One of them is switching to the data science team in your current organization.

Let me explain why you should focus on this rather than other paths (at least for starters).

You already know how things work in your domain.

Faced with certain variables in a dataset, you are quite adept at dealing with them since you have the required business knowledge Your leadership and management team is already familiar with your performance and work ethic.

They know what you bring to the table – trust is a big factor in any team, especially a data science one.

That works to your advantage No need to spend time scouting potential work opportunities outside your organization.

Everyone dreads that time scrolling through job portals and what not in the slim hope of finding a decent opportunity This might not necessarily apply to everyone, but you might not see a sharp salary jump (if any) when you switch organizations.

Remember, you are transitioning to a few function where you had limited experience I could go on, but you get the idea.

Always make it a first preference to look for opportunities in your current work place.

Talk to folks in a senior role or from the data science team.

Build up your network and trust me, it does pay off eventually.

  Challenges and Solutions: Unable to find an opportunity in my current organization: Fair enough, you gave it a good shot.

It wasn’t meant to be.

If this happens, then you should definitely cast a wide net.

As we discussed in the preceding step, your LinkedIn network and the data science community will come in handy.

Don’t stick to passive job searching through online job portals – reach out to hiring managers through LinkedIn and other professional networks.

Showcase your projects/portfolio online.

And don’t give up!.It will test your patience but the first breakthrough is worth the effort   End Notes That was quite an exhilarating journey!.I have made this transition myself quite a number of years ago.

I have seen this field evolve over time and my aim in this article was to help you make that switch.

You already have a number of steps covered that most aspiring data science professionals don’t, so make it count!.If you have any questions on this learning path, or any feedback on this article, let me know in the comments section below.

Meanwhile, here are a few additional resources to learn data science and give yourself the best chance of breaking into this field: Introduction to Data Science Course Ace Data Science Interviews Course Certified Program: Data Science for Beginners (with Interviews) You can also read this article on Analytics Vidhyas Android APP Share this:Click to share on LinkedIn (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on Pocket (Opens in new window)Click to share on Reddit (Opens in new window) Related Articles (adsbygoogle = window.

adsbygoogle || []).


. More details

Leave a Reply