What is data science? And how its used in machine learning
With tremendous data being generated every second, it is not difficult to imagine the potential of the many vital insights hiding in the data. Today, organizations focus on analyzing this collected data to discover insights into crucial business-related questions: How did the sales perform against estimated target sales in the last quarter? Are older customers contributing more to sales? Which customers should be given coupons? Let us understand how data science is helping organizations answer questions like these.
Data Science is an extensive topic covering many aspects. It uses statistics, mathematical models, and data analysis to extract value from raw data. It is a subset of artificial intelligence and is providing a competitive edge to data-driven organizations using analytics. In simple terms, it is acquiring and storing data, refining, and cleaning, analyzing, and visualizing it to help the business evaluate its performance, forecast, and make better data-driven decisions.
Vast amounts of collected data and untapped valuable insights make data science very popular as a tool to analyze raw data. We can see how data science fits in the business data analytics cycle (BDA).
Some examples where data science is being used along with machine learning to improve services and products are:
- Prediction: Loan payoff prediction looking at similar previous borrowers or predicting overall rating of cell phone companies based on reviews and many more, customer churn prediction.
- Classification: Classifying whether a customer should be given coupons based on his buying history or whether an email is a spam or not.
- Forecasting: Forecasting sales of a particular department or forecasting revenue growth if a new branch is opened in a specific location.
- Pattern Recognition: Facial analysis to reduce crime rate or checking for plagiarism.
- Clustering: Inclination of voters, profiling, and clustering Facebook users for potential customers.
- Segmentation: Customer segmentation based on their buying behavior or deciding marketing strategies based on segments of customers.
- Analysis: Analyzing web and social media engagement statistics using descriptive analytics.
Broad skill requirements
Some of the broad skills required by a data scientist are mathematics and statistics, machine learning, SQL, programming language like Python or R, visualization, an analytical mindset, and domain knowledge.
Data science in the BDA cycle
Before doing the actual extensive data analysis using mathematical and statistical models, machine learning, and artificial intelligence, exploratory data analysis is performed. It involves the identification of initial trends and relationships to develop a reasonable understanding of the data. The data scientist and the business analyst collaboratively identify the data gaps, interrelationships, data that needs to be cleansed, or outliers that indicate data exclusion based on domain knowledge and business requirements. Data integrity, validity, reliability, and bias are used to assess the data quality. Furthermore, data is analyzed to check if it is suitable for the actual analysis and is aligned with the business requirement. Some examples include histograms to check variable distribution and skew, boxplots to identify outliers, and heat maps to show interrelationships.
Different visualization tools like Tableau are used for exploratory analysis. Currently, Tableau is one of the most powerful and fastest-growing Business Intelligence and visualization tools in the industry. Tableau Ccertification Training is also one of the top most requriment in organizations. It is changing intuition-driven decisions to data-driven business decisions. Tableau’s interface is designed for a natural and seamless flow of thought and actions. It is empowering millions of users and organizations to understand their data.
After an initial understanding of the data, the data scientist can then decide the most suitable mathematical model, approach, and parameters for analysis. Here, the business requirement is transformed into a mathematical question and model for deeper analysis.
After the data analysis, the insights revealed are communicated to the stakeholders by the business analyst to enable them to make data-driven business decisions. These insights are delivered such that they are easily understandable, appeal to the stakeholders, and impact insights into actions.
To create explanatory visualizations for non-technical stakeholders, Tableau is very helpful. Tableau’s user-friendly interface helps in data storytelling and compellingly presenting information with pretty and easy-to-understand graphs, dashboards, and stories.
Data scientist and business analyst in BDA project
For a data scientist to work successfully in a business data analytics project, it is essential to understand the end-to-end business cycle and visualization tools like Tableau; and for a business analyst, it is vital to understand the basics of statistical and mathematical models, machine learning, Tableau to perform exploratory data analysis and present insights using dashboards and stories.
IIBA – International Institute of Business Analysis, a non-profit professional association serving the growing field of business analysis, provides a specialized certification in Business Data Analytics. Earning the CBDA certification informs employers of your passion for and competencies in performing business analysis on analytics initiatives.
Certification in Business data analytics will give a rapid start to your journey. It will provide you with an in-depth understanding of how analytics is used in different business domains and how you can work towards data-driven decisions. You can begin by attending a classroom or online course. It will prepare you for the certification exam with mock questions, flashcards, and drills and train you in working on real-life case studies and projects. Also, learning Tableau with this certification will prove beneficial. Tableau training can give an in-depth understanding of different features of Tableau and how to use them in various business scenarios. You can begin by attending a classroom or online Tableau course. It will give you hands-on practice on real-life case studies and projects.
Once you equip yourself with the right skills and an in-depth understanding, a certification will unquestionably give you an added advantage.
Story by Priya Telang