Data is an important element in today’s world. Data could be generated through social media, web browsing, and tons of mobile applications that are being used across the world. Many businesses today collect data about their customers and website users to gain more insights about them. This data helps them understand their audience and improve their business. Additionally, taking a Data Science Course can provide individuals with the skills and knowledge needed to analyze and make sense of this vast amount of data.

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Data science is a multidisciplinary approach with which organizations extract meaningful and actionable insights from large volumes of data. According to IBM, data science may combine the scientific method, specialized programming, math and statistics, advanced analytics, AI, and even storytelling to uncover and explain the business insights that are buried in data. In this article, we are discussing the top 5 things that you must know about data science.

#1 How is Data Science different from Business Analytics, Business Intelligence, Data Mining, and Predictive Analysis?

You must have heard all these terms at some point or the other. Let us see what they depict. Are they all similar to data science or is there any difference?

Business Analytics: Businesses often use statistics to extract insights from their data and use those insights in their everyday decision-making. Data Science is a superset of business intelligence or we may say, it is a step ahead of business intelligence since it involves programming, advanced analytics, AI and scientific methods to extract insights from data.

Business Intelligence: Business intelligence is a descriptive analysis that tells business owners what has happened. For instance, business intelligence can be used to understand the market or how we may stay ahead in the competition. Data science, however, is predictive analysis. It tells what will happen.

Data Mining: Data Mining is just a technique that helps in finding patterns in data. However, data science is an area. Data mining helps in understanding trends hidden in data while data science helps in social analysis, building predictive models, and more.

So, to leverage data science in the medical field, institutions should heavily focus on gathering the right data, labeling it properly, and implementing it in the future.Data mining could be considered as a subset of data science.

Predictive Analysis: As the name suggests, predictive analysis is done to forecast what will happen in the future. It helps in predicting the future of the company. Data Science, on the other hand, is about obtaining information from the existing data by organizing and maintaining data.

From the above definitions, it is clear that data science is a much broader term than Business Analytics, Business Intelligence, Data Mining, and Predictive Analysis. One might need to gain experience in any one of these fields before they become a data scientist.

#2 Who is a data scientist?

Data scientists are people who are responsible for extracting useful insights from large data sets and solve complex problems of an organization. Their responsibilities include defining which data sets are relevant and then collecting data from various resources. They create and apply algorithms to implement automation tools. They research the industry and company to identify areas of improvement, efficiency, and productivity.

Data Scientist is the most in-demand job role in the UK and US. To become a data scientist one must learn a programming language like Python or R. They must know statistics and SQL. They will also have to learn machine learning algorithms. One may earn a bachelor’s and/or a master’s degree in IT, computer science, math, physics, or another related field.

#3 Who is a data analyst?

Data analyst is another job role in data science that is quite a in demand these days. Data analysts are people who gather data and interpret it to solve a specific problem. On a day-to-day basis, data analysts perform the actions of gathering, cleaning, modeling, interpreting, and presenting data.

Analysts collect data through surveys, web analytics, or data sets. Then, they clean the data by removing redundancy and errors. Then, they perform data modeling by creating the database. They may choose how to categorize data and find patterns and trends in the data. Lastly, they communicate their findings using charts, graphs, and reports.

To become a data analyst, one must earn a bachelor’s degree that includes the study of statistics, maths, and/or computer science. You may learn some data analysis skills or earn certification in data analysis. Then, you may pursue a master’s degree or start with an entry-level data analysis job.

#4 What is the Data Science Lifecycle?

Data Science Lifecycle or Data Science Pipeline includes overlapping, continuing processes that represent how data is transformed into useful information. The data science lifecycle may involve up to 16 processes. Let us understand the main processes.

• Capturing: This stage involves gathering data (raw and unstructured) from various relevant sources. It may include data entry, web scraping, and capturing data from systems and devices.
• Preparing and maintaining: This stage involves converting the raw data into a consistent format for analytic or machine learning. It may include cleansing of data, reformatting, removing redundancy, and other data integration technologies.
• Preprocessing or Processing: This stage involves an examination of biases, patterns, ranges, and distribution of values in data. At this stage, it is determined whether data is suitable for predictive analytics, machine learning, and/or deep learning algorithms.
• Analysis: This stage involves the discovery of data. Here data scientists perform different types of analysis such as statistical, regression, machine learning, and deep learning to extract insights from data.
• Communication: At this stage, data scientists or data analysts present the results in the form of charts, graphs, reports and presentations.

#5 Data Science Trends

Two programming languages are quite common in Data Science: R and Python. R is an open-source programming language that is suitable for producing graphics and statistical computing. It has a variety of tools that are used for preparing, cleaning, and visualizing data.

Python is an object-oriented, general-purpose, high-level programming language. It is easy to learn, scalable, and offers a wide variety of data analysis and data science libraries. It also comes with a variety of visualization options.

To become a successful data scientist, you must learn R and Python programming languages. You must also be proficient in using big data processing platforms such as Apache Spark and Apache Hadoop and visualization tools like Tableau and Microsoft PowerBI.

Now, new and better trends are emerging in the field of data science since businesses get more and more dependent on data. It is believed that data science would help in accelerating change in businesses. Let us see what are the major current and future trends in data science.

• Augmented Analytics: Now AI (Artificial Intelligence) and Machine learning along with Natural Language Processing is being used to enhance the data lifecycle. This technology would take less time to process data and we can expect more accurate results. We may also expect in-depth reports and better visualization.
• Edge Intelligence: We are generating data more than ever. Therefore, storing all that data on the cloud and then extracting useful data out of it makes things quite cumbersome. Industries are therefore taking advantage of the internet of things (IoT) to keep data where it is generated. This means no storage of data on the cloud. Instead, the cloud is just notified when the data is processed and required action is taken.
• Automation Everywhere: Hyper-automation is another trend that harnesses the power of technology to create meaningful insights from data. Scientists have now combined AI, Robotics, and Machine Learning to automate complex processes where once experts were needed. Everything will be done by bots now.
• More focus on Python: If you are thinking about learning R or Python for your data science career, choose Python. It is a lot easier than R and is suitable all-rounded for a range of business types. You can do a lot more with just a few lines of code. Last, but not least, Python supports free data science and machine learning libraries.

Bottom Line

Data Science has been here for quite some time and now the industry is booming more than ever. According to Big Data and AI Executive Survey 2021, 39.3% of companies are using data as an asset while 24% have already created data-driven organizations. In this article, we discussed how data science is different from some common terms such as business intelligence, business analytics, data mining, and predictive analysis.

We also discussed the main job roles of data scientists and data analysts. We shed some light on the data lifecycle and current trends in data science. It is quite clear that scientists are willing to combine technologies like machine learning and artificial intelligence to automate the data science lifecycle thereby enhancing its accuracy and speed.

There is a lot more to experience in the field of data science as the innovations are not over yet. So get ready for businesses that provide a more personalized and high-quality user experience using the latest data science techniques.