Business analysts are hired on the basis of their projects submitted during the various stages of pursuing a business analytics course. In the data science industry, we all know that merely skills wouldn’t take you far—it’s the way you manage a business analytics project that helps you score all the good points in the job interview. So, how exactly do you ace the job interviews by virtue of your project? Well, start on the correct foot whilst planning your business analytics course project. In this article, I will highlight the smartest tips that would help you master your project management goals within the shortest possible time and resources.
Step 1: Start Building your Data Science Network
Real networking in the data science community always revolves around projects, and not on skills or course structure. Analysts and scientists extensively discuss the various aspects of business analytics and how different technologies and capabilities can help in planning, executing, and evaluating the project objectives.
There are some highly revered data science communities in the open-source as well on social media, such as on LinkedIn, Twitter, Quora, and Reddit.
Top business analyst project discussion topics more or less look like these:
- Sales Forecasting in a Holiday Shopping Season
- Product Recommendation Index of a highly successful E-commerce platform
- Top ranking Hashtags and their performance in social media communication
- Effective budget forecasting of Integrated Communications Management teams in the corporate world
- Sales predictions of airlines during the pandemic season
- Hotel booking and accommodation percentage in peak and lean seasons
- Employee turnover ratio in the company versus industry trends
- Sales optimization of automobile parts company in lean manufacturing season
- Insurance premium collection rates during moratorium phase in a recession economy, and its effect on GDP
You can choose any meaty topic to crack the code and find new ideas to solve complex challenges from the real world scenario. Tools such as Tableau, Microsoft, SAS Analytics, and Sisense are helping business analysis groups to use automation decks to build sequences for all types of BA projects.
Step 2: Join a BA Workshop
In this hyper-personalized world, it would be a huge loss of opportunity if analysts don’t know what a workshop is and how it effectively enables professionals to drive meaningful relationships. Merely registering in a course wouldn’t guarantee an interview spot for you. Yes, there are ways analysts leverage courses to scale through interviews, but in the long run, practical knowledge acquired during project management really works well. And, workshops are the best medium to enhance working with data interpretation and problem solving for various analyst jobs.
If you want to master the project management stage during business analytics course, join with at least 2-3 workshops that cover all the topics in statistical and qualitative analysis, in addition to providing hands-down training on forecasting techniques used to improve decision making for business operations, such as in Marketing, Sales, Finance and Accounting, IT security and HR.
Step 3: Pick top 5 Analytical Tools and Gain Certifications
There are hundreds of best in the class breed of BA BI tools that you would probably come across in your career as a trainer. But, let’s focus on the top analytical tools. Reason: these are leaders in the services and solutions offered to BA teams, and most likely these would be using AI and machine learning capabilities in the same form or the other which makes your job easier when you are designing your projects for business analytics.
By virtue of popularity, you would always find MS Excel, KNIME, and SAS among the top business analytics tools. But, let’s not keep our vision restrained on these alone. During the training phase, experts recommend that students should start working with Tableau, Python, R, and Splunk platforms, which are most advanced in their capabilities as far as analytics with machine-generated data is concerned. From text analytics to NLP designing, these tools can bring a whole new range of real-life data transformations for data mining, data pipelines, and so on.
Other prominent platforms that you should know are Oracle, Sybase, Ingres, IBM Red Hat, My SQL, Teradata, and so on.
Step 4: Prepare your elevator pitch
An elevator pitch is your 15 seconds fame in data science interviews. Practice your elevator pitch basis your work in business analytics and your knowledge in handling diverse tools mentioned above. Within 8-10 months with workshop management in mind, you can land a great job.