Human society has changed immensely over the last few decades. This change can be attributed to breakthroughs that happened during the previous four decades. It all started with the invention of increasingly faster computers, the internet, smartphones, and artificial intelligence. All of these inventions mark a fundamental pivot in human society that charted the future technological and sociological development path. Contemporary data science techniques rely on these groundbreaking technologies. It has helped humans increasingly analyze more extensive data sets and find meaningful insights. Looking at the exponential increase in data science utilization across all the major industries, data science will be one of the most lucrative and promising career paths. This article will shed light on data science certification courses.
The rise of data science
As a field of study, data science combines various topics into one. It includes mathematics and programming to create analytical programs and analyze increasingly larger data sets. People use data analysis for various reasons. Yet one will find the underlying cause of data analysis to be the same across the board.
Data analysis is generally divided into four distinct categories. Descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis.
Descriptive analysis is the most basic and old data analysis method. You might have used it on a personal level to track past performances. Every company or business uses descriptive analysis. The analysts will gather information about a company’s key performance indicators and analyze them to find how the company performed over a set period. Businesses usually do this analysis every quarter. Comparing data from the recent quarter to the previous quarter gives a general idea about the company’s performance.
A company or business can acquire data about its performance through descriptive analysis. But it only gives a general idea about the increase or decrease of key performance indicators such as total revenue and profit margins. So is it better if somehow they can understand what decisions worked in their favor and what worked against them?
The diagnostic analysis takes on the challenge of finding the answer to why the critical performance indicators moved a certain way.
3. Predictive analysis
Unlike the previous two analysis methods, predictive analysis requires sophisticated software and algorithms. As the name suggests, predictive analysis is an analysis method that tries to predict the future. We all know how bad we are at predicting the future. The human mind can not comprehend the vast number of things that affect the future outcome. This is why data scientists use artificial intelligence to develop machine-learning algorithms that can take in various data points and calculate future outcomes with reasonable accuracy. Predictive analysis is an excellent tool for any company or business to protect itself against the volatility and uncertainties of the market.
4. Prescriptive analysis
The final and most advanced form of data analysis is called predictive analysis. It is one of the most powerful data analysis methods because it takes away the need for human interpretation from the equation. Humans tend to have biases, and it clouds our thought process. But machines do not have to deal with petty human problems like psychology and prejudice. Instead, data scientists develop deep learning algorithms that can take in mind-bogglingly large data sets practically incomprehensible to humans. These deep-learning algorithms analyze the data sets in greater depth and find patterns. The analysis results are then used to make informed decisions for the company or business.
Prescriptive is still used by only the more prominent players because of the need for more technical knowledge and ability. Being able to use prescriptive analysis advantageously positions these companies against their competitors. Prescriptive analysis can be used to increase the overall efficiency of production, streamline supply chains and find actionable insights that have seemingly no relation to the business.
Data science certifications, structure and fees
Data Science certifications are focused on teaching the various aspects of data science in a way that helps the students in developing their skill sets as data science developers and analysts. These courses will follow a set curriculum, including mathematics, programming, data cleaning, artificial intelligence, cloud computing, and dashboard creation. Let’s discuss these in detail.
Mathematics plays a crucial role in data science. Modern data science, on a fundamental level, is an upgraded version of statistics. Therefore, to become a capable analyst, one must be aware of some mathematical concepts such as statistics, probability, linear algebra, and calculus. Thus, data science certification courses will have them included in the curriculum.
Modern data science relies on developing various algorithms to take on the challenges of data analysis, specific to the needs of a company or business. Therefore, most data science certifications include a crash course on python. Python is considered the holy grail of programming languages. It is not just easier to learn when compared with other relevant programming languages, it also has a vast community-driven library where one can find all the resources one needs.
Data cleaning and structuring
Acquiring data is complicated and polarizing. For all practical purposes, let’s not get into that rabbit hole. All you need to know is that data can be acquired through various means and methods. Now data comes in all sizes and formats. But before putting them through the analysis process, one must clean them of all irregularities. Data analysts have to take on the job of cleaning and structuring the data to make it palatable to the algorithms and software. Abnormalities in the data will lead to inaccuracies in the final results. This is why data science certifications focus heavily on teaching the tools and techniques that enable quick and easy cleaning and structuring of the data sets.
The cutting edge of modern data science and analysis relies on artificial intelligence for the timely disposal of in-depth research. Both predictive analysis and prescriptive analysis require artificial intelligence for the analysis. Machine learning and deep learning algorithms are mainly used for these types of data analysis. Data science certifications include classes on machine learning and deep learning algorithms. It teaches the students the process of developing these algorithms.
Cloud computing has become an integral part of data science and analysis. When working with enormous data sets, one must deploy sophisticated computational hardware. Without them, the analysis process will take months, if not years. Managing and maintaining this sophisticated computational hardware can take time and effort for most businesses. Cloud computing services provide companies with the required computational power at a fraction of the cost and without the headache of managing servers and whatnot. Therefore data science certifications will include classes on cloud computing to familiarize the students with various cloud computing services and how they can use them effectively.
Data analysts will have to present their findings to the managing directors or responsible authorities. But how does one explain complicated topics in a concise and easily understandable format?
Visual representation is an effective way to present complicated topics. This is why data analysts create interactive dashboards to get their ideas through to the relevant authorities. Therefore, data science certifications take great care when teaching the various tools and techniques used for creating superior dashboards.
Fees for data science certifications
Fees for data science certifications range wildly from course to course. Course pricing depends on the location and the institutions offering them. However, on average, a data science certification course will cost between 30000 Rs to 70000 Rs.