What does it take to complete the data science course like a pro?

If you are flirting with the idea of becoming a data scientist, you are likely to enquire a lot about the data science course duration and data science topics required to become a master professional from a fleeting beginner. There has been a 500 percent growth in the demand for business roles and projects headed by certified data scientists and analysts. 

The data scientist job title continues to be among the top 5 best ever business roles as far as popularity, compensation and future horizon are concerned. Data science is not only a competitive space to work in but also requires a reasonable training from top data science courses. 

In this article, we will discuss the different ways and tactics you could adopt to complete a data science course like a pro, and the time and effort needed to do it with successful outcomes. 

Step 1: Learn how to import data 

The data science course duration could be reasonably influenced by how fast you can learn to import and export data from sources or origins to the final destination. 

What is an import of data and why is it so important for data scientist aspirants?

Data import is nothing but the scientific process of collecting data from different sources and integrating these with the existing data sets from analytics and warehouses. The larger the reliance on external data sources, the longer would be the duration of your data process. There are many different tools for importing data, and one of the most reliable is Google Analytics. 

More than 90 percent of the analysts and scientists work with Google Analytics data to match various business points. These could also work with other sources of data extracted from Google products and services, like the YouTube, Google Maps, Google Pay, etc. Other companies that follow a similar concept of data import are Facebook, Twitter, Uber, Salesforce, and Flipkart.

Step 2: Segmenting different types of data

Learning how to segment different types of data is a massive task. The basic differentiation is that of structured versus unstructured data or unlabelled data. The larger the database of your unstructured or unlabelled data, the longer would be your duration of the data science course. That’s why you should be really adept at learning data import in your first few weeks of data science training. This mastery would allow you to understand how different data types behave and influence the outcomes of your data science projects.

The different types of data based on their origin and behavior are as follows:

·        User data: public or private data

·        Enterprise data: internal or external data

·        Campaign data: from Marketing and Sales campaigns

·        Customer data

·        Location data / geographical data

·        Product data, such as those listed on Amazon and Flipkart e-commerce sites

·        Financial data

·        People data, as extracted from the company’s employee database

·        Healthcare data, etc

Depending on the industrial use and demography of the customers or users, the analytics team could further improve the extent of your understanding of data types segmentation and their use in the larger business intelligence products. 

Step 3: Code with top programming languages for data science

There is no denying that data scientists may not require to master data programming or coding for their projects. But, if you are to become a top ranking data scientist at the end of the course, you must embrace a bit of coding skill. Python, Matplotlib, R, and others that are popular among data analysts are also very useful for data scientists. However, if you are adding programming skills from the scratch, your duration of the course could increase by 8-10 weeks more. 

In retrospect, when you complete the data science course duration, which could be anywhere between 2-3 years, you will reap the full benefits of having been great at programming with Python and R. So, don’t ignore the importance of having invested time and effort with programming languages specifically designed for managing data science projects.

Step 4: Practice with the enterprise data science tools

There are many types of data science tools and solutions available online for beginners and experts. You could sign up with the companies that offer training and resources in data science. Many courses also offer collaborative platforms to train aspirants in data science and data ops for a better understanding of how teams work in real life scenarios. The lab simulation allows analysts and learners to quickly ascertain how their theoretical knowledge would help accelerate their projects and thereby optimize course duration.