Data is essential in 2023! And thankfully, the same is abundantly available for effective and strategic use. Humanity also possesses the power to process these huge amounts of data into sensible prescriptions, that is formed with rapid-onset ordeals in mind. Therefore, the commercial and public sectors alike are trying to use as much data as possible. And the value of a data scientist is gradually increasing in all sectors.
Naturally, the responsibilities that a data professional is expected to handle are crucial for the survival of their employers. Therefore, they must possess the right set of skills and put ample effort into testing them in real-world scenarios. And the same can only be assured by a responsible institute offering an updated data science course syllabus that is aligned with industry expectations. This article will discuss the essential offerings of a good data science course syllabus. So that the potential students might never end up with an irrelevant data science course, unworthy of temporal and financial investments.
1. Understanding Exploratory Data Analysis
Exploratory data analytics (EDA) aims at the discovery of patterns and trends by using machine learning and deep learning tools. An essential skill every data scientist must possess.
Coursework on EDA must aim to arm the students with the skills needed for;
- Identifying and optimizing the source of data
- Identifying or removing outliers
- Identifying spatiotemporal trends
- Discovery of patterns in the data
- Forming a hypothesis and testing the same
2. Learning the implementation of Machine Learning tools
The data volumes and diversity a data scientist deals with in 2023 are immense. And due to the diverse utility of the discipline, data scientists handle data from a massive variety of genres and sources. Therefore, humanly impossible analysis and visualization tasks must be outsourced with machine learning tools. And data scientists must possess the skills to easily develop and deploy them.
3. Learning Model selection and evaluation
The automated analysis model must be chosen based on the operational requirements. And as per the approach of analysis. And a wrong selection can result in an accumulation of errors and misdirected analysis.
4. Mastering Data Warehousing
A data warehouse is a paradigm for the collection, processing, and visualization of analysis, all through building a comprehensive database. There are three major types of warehousing paradigms, enterprise data warehouse (EDW), operational data store (ODS), and data mart.
5. Becoming adept at Data Mining
Data mining is concerned with the prediction of future trends and events through the identification of trends and patterns of the past. And the same involves large-scale interpretation and analysis ventures that might also involve automation tools like machine learning and deep learning entities.
6. Data Visualization
Data visualization aims at explaining the analytics insights in lucid and easily comprehensible language. So that the involved individuals can understand the expectations of their employers and work as much and with as much finesse as they must. A data scientist is expected to learn the use of visualization tools for a clear representation of analysis results. So, the big picture is clearly explained to an average investor or an employee.
7. Cloud Computing
Cloud computing means internet-based computations. Today, essential services like analytics, intelligence, networking, servers, and databases can be availed from the internet. And the same is more flexible and allows more room for innovation. Thus, a data scientist must understand how to utilize public clouds, private clouds, multi-cloud, and IT clouds to ensure an effective analysis.
8. Business Intelligence
Business intelligence is the collection of techniques, tools, paradigms, and infrastructure a business utilizes for harnessing the latent power of data. The goal is to collect, process, analyze and derive prescriptions from huge amounts of all kinds of data. So that the decisions remain effective and the ordeals can be mitigated with impressive ease. A data scientist interested in working in the commercial sector thus must master business intelligence.
9. Storytelling with Data
Stories are memories, and the same can define a phenomenon the best! Therefore, a responsible data scientist is expected to possess the skills of forming a story with the help of data. A story is clear enough for everyone to understand and remember easily! A data professional must know how to effectively use visualization tools for enhancing the story. And dramatically reveal the patterns and trends so that the gradual arrival to the conclusion remains as engaging as possible. A story must conclude with approval or disapproval of a hypothesis. And a clear indication of the recommended institutional behavior!