Data science is not a new word. It has been used since the 1960s in academic circles. At that time, it was used as an alternative term for statistics. But, outside of academia, the word was rarely used. It all changed when the 21st century rolled in. Due to the increased computational power and advanced technologies, data science took off as a new field of study that can help businesses. Since then, the popularity of the field has increased exponentially, and the future of data science is as bright as possible.
This article will describe data science comprehensively by answering five simple questions.
- What is data science?
- What does a data scientist do?
- What are the most common Data science challenges?
- What are the Data Science course duration and fees?
- How to choose a suitable data science course?
- Excel programming
What is data science?
Data science is the discipline of collecting, analyzing, and presenting data in the form of insights and dashboards to make it accessible to the directors and managers of a business.
Data science combines various other fields of study to achieve this goal. Data science courses will teach maths, machine learning, deep learning, cloud computing, statistics, and programming.
The role of a data scientist
Data scientists work in a structured manner, and they will follow a step-by-step process.
The very first step in the process is collecting data. According to the business’s needs, data can be collected from various sources. An organization can buy data from different data collection organizations. It could use organization data, like sales figures, number of products sold, number of buyers, etc. Other external data sources are also available.
After the data has been collected, the number one priority is to clean the data. This is done to remove any inconsistencies from the data sets. Without this process, the results could become screwed or unusable and render the whole process worthless. This is why data scientists take great caution while the process is ongoing.
After cleaning the data of any inconsistencies, the next step in the process is to analyze the data. Data scientists will run the data through various software and technologies to gather insights. The data analysis is divided into two simple and two advanced categories. These are Descriptive and diagnostic analyses and predictive and prescriptive analyses. The first two are relatively more straightforward and use elementary techniques and technologies. The other two are considered advanced forms of data analysis and will use modern technologies like machine learning. Let’s discuss them briefly.
Descriptive analysis
Descriptive analysis is the primary and most commonly used form of data analysis. Descriptive analysis is done while keeping one goal in mind to answer the question, what happened?
Giving data in a visual and easily understandable manner is essential. The data scientist will use software such as excel to process the data and gain insights from it. These insights will then be presented in the form of dashboards that include various types of graphs. The directors and managers can get an overall idea of the business’s key performance indicators by glancing at the dashboard.
Diagnostic analysis
The diagnostic analysis looks to answer a crucial question: Why did something happen? If the business can not identify what is going in its favour and against it, how will it improve its business? Without this answer, it is practically impossible to take justifiable actions.
There are various statistical methods that the data scientist will use to explain the reasons to the stakeholders. Some more commonly used methods are the drill-down method, data mining, data discovery, and correlations.
Predictive analysis
Predictive analysis is done to project a company’s future performance potential. This form of analysis answers the question, what will happen next? For predictive analysis, data scientists will use machine learning. It is also used extensively for weather forecasting.
Prescriptive analysis
The prescriptive analysis is the most advanced form of data analysis. The data scientist will use machine learning and deep learning technologies to gain valuable insights from the analysis.
The prescriptive analysis is done to find a future course of action for the company that can yield valuable results.
After the analysis, the data scientist will work with managers, explain the insights to them, and chart a course of action according to the study.
Challenges a data scientist deals with
Structuring the data is the biggest and most prevalent challenge faced by data scientists. Cleaning the raw data takes a lot of time, leading to mental exhaustion.
Another challenge faced by data scientists is computational power. The algorithms must go through large amounts of data for predictive and prescriptive analysis. Higher computational power will get the job done quickly. There are cloud computing services available. Which kind of mitigates the problem.
Communication is another challenge faced by data scientists. The data scientist’s job is to communicate the gathered insight to the relevant departments. Sometimes it could become very challenging if the managers are unaware of the data analytics process.
Data science courses: what to expect?
Data science courses go over the basics of data science. This is especially helpful for beginners with little to no data science knowledge.
It will include classes on software like excel, tableau, and SQL. This software is used extensively for creating dashboards and managing databases.
It will also include classes on a programming language. Usually, the course will have classes on the basics of python and then classes on data visualization and analytics with python.
The course will teach machine learning to some extent and also the process of predictive and prescriptive analysis.
The course will offer classes on other ‘A.I’ technologies and cloud computing. Cloud computing helps in outsourcing the computational powers from cloud-based servers.
A good data science course will have industry-aligned classes. It will help the students immensely as having practical knowledge about the implementation process is much more important than having a course certificate.
Data science course duration and fees
Data science courses are available as both online and offline classes. The data science course duration will be around 4 months. It will cost anywhere between 30000 to 40000 rs for the entire course.
The e-learning courses are generally cheaper by 10000 rs or more, yet they are no less effective than regular classes. For mid-career managers, e-learning classes are a great opportunity as it allows them to keep their current job while pursuing the course.
How to find a good data science course offering?
With the increase in demand, many subpar institutes have popped up from nowhere in recent years. It has become quite confusing for people to find a good course.
While opting for a data science course, one should try to find answers to these questions,
- Is the course offered by a reputable institution?
- Is the course moderately priced?
- Does the course cover all the topics that we have mentioned above?
- Is the course industry aligned and focused on teaching the practical use of the technologies?
- Does it have an experienced faculty?
One should run the course through this filter, and if the answers to these questions come out as yes, then one can opt for the course.
Excel programming is the process of creating a set of guidelines that teach Excel to carry out one or more activities. These guidelines are created in Visual Basic for Applications (VBA), the only writing system that Excel can comprehend.