The Anatomy of a Good Data Science Course

If you are a data science enthusiast, you would have noticed that the internet is flooded by hundreds of e-learning platforms offering data science courses. If you are a beginner, looking to enroll in one of these courses, one of the major criteria to make the choice is to find a curriculum that covers all the concepts necessary and a program that will provide you with hands on experience through various projects to make you a well qualified and a well taught Data Scientist. So what does a good data science course comprise of ? Here’s the real deal.

To begin with, one should first be well versed with each of the steps involved in Data Science aka the data science lifecycle., the five stages of a typical data science lifecycle being Data Acquisition, Data cleansing, Data Analysis, Data Visualization and Data Interpretation. So for a data science course curriculum to be complete, it should include all the necessary tools and resources that are required to complete these five stages. So let us begin by segregating all the tools and resources that are required at each of these stages.

Data Acquisition: In order to get one’s hands on the required data sets, querying of databases has to be done as the first step of data science and in order to perform querying, one needs a good grip on technical skills like MySQL or MongoDB. These querying languages will get you to learn database management. Remember that different languages are useful in different scenarios. For instance, MySQL is used for working on structured databases whereas NoSQL or MongoDB are used to work on non-structured databases. So if your Data Science course includes one or more of these, then you are good to go.

Data Cleansing: The data acquired from databases may usually consist of irrelevant or non-uniform data, which we can safely mention as junk or garbage data. This data needs to filtered, or rather cleaned, for one to get the most accurate results. Though there a few open-sourced tools such as OpenRefine available to automate this process, it is important to know how the cleaning is done manually and for this, scripting tools such as Python or R come into picture. These tools will help in converting file formats to one standard format and also helps in extracting and replacing missing data sets. When working with bigger data sets, tools like Hadoop or Spark are often preferred. So if you do not find any of these scripting languages included in a data science course you are looking for, then it definitely is an easy pass as scripting languages lay the foundation to data science.

Data Analysis: Now that you have the cleaned and filtered data at your disposal, you need to understand the data. So the next step in the data science lifecycle is data analysis. In this step of data science one needs to understand the properties and features of the data in correlation with the business problem at hand. So in order to be well versed with data analysis, make sure that your data science course curriculum includes concepts like Numpy or Pandas within Python or tools like GGplot2 in case of R.

Data Visualization: Once the data is gathered, filtered, analyzed and understood, it then has to be modeled and represented in a certain way to dig out the underlying patterns aka the solutions to the business problems at hand. Hence it is safe to say that data visualization plays a key role in interpreting the data. Data visualization involves designing and training models to classify and segregate data. This is done using clustering algorithms to identify similar sets of data points. Data Visualization uses machine learning concepts of regression and prediction. So the technical skills that one needs to know for this stage of the data science lifecycle are accessing and using libraries like scikit-learn in Python or CARET in R.

Data Interpretation: Whether or not you realize, one of the most crucial steps in data science requires excellent communication skills to be able to translate and communicate the interpreted solutions. Hence the A1 skill required at this stage is a strong business domain knowledge. Apart from this, data visualization also comes to be a part of this stage so tools such as Tableau are used.

The bonus tool: A good data science training program should also help you tighten your grip on Microsoft Excel. Though Excel is no longer used to perform analysis, it is just one of those old school methods of analysis which everyone needs to be well taught in.


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Dedeepya Bypuneedi

A Computer Science graduate by education and a content writer by profession. Currently fulfilling her zeal to write by putting pen to paper every time she comes across something that is interesting enough to let the world know