11. Introduction to Analytics

Foundation Course on Information Technology Outsourcing UNIT -11: INTRODUCTION TO ANALYTICS Structure 11.0 Learning Objectives 11.1 Introduction 11.2 Basics of DataScience 11.3 Basics of Data Analytics 11.4 Summary 11.5 Glossary 11.6 References 11.0 Learning Objectives After studying this unit, you will be able to • Outline Data Science basics • Recall fundamentals of Data Analytics 11.1 Introduction Data analysis involves sorting through massive amounts of unstructured information and deriving key insights from it. These insights are enormously valuable for decision- making at companies of all sizes. Data analytics is the science of analyzing raw datasets in order to derive a conclusion regarding the information they hold. It enables us to discover patterns in the raw data and draw valuable information from them. Data analytics processes and techniques may use applications incorporating machine learning algorithms, simulation, and automated systems. The systems and algorithms work on the unstructured data for human use. These findings are interpreted and used to help organizations understand their clients better, analyze their promotional campaigns, customize content, create content strategies, and develop products. Data analytics help organizations to maximize market efficiency and improve their earnings.

Foundation Course on Information Technology Outsourcing 11.2 Basics of Data Science Data Science is a multi-disciplinary feld that uses scientfic methods, processes, algorithms andsystems to extract knowledge and insights from tremendous amount of data. Theories and techniques from many felds and disciplines are used to investigate and analyze a largeamount of data to help decision makers in many industries such as science, engineering, economics, politics, fnance, and education Data Science Essentials Data Science Essentials Data Science Life Cycle • Business Understanding-Every domain and business work with a set of rules and goals. In order to acquire the correct data, we should be able to understand the business. • Data Collection-The source ofdata could be logs from webservers, data fom online repositories, data fromdatabases, social media data, data in excel sheet, so in short data can come from any source.

Foundation Course on Information Technology Outsourcing • Data Preparation-To analyze the data, data needs to be in certain format. Data might have missing values which willcause obstruction in analysis and model building. • Data is to be cleaned before processing any further. Thus, this step is also known as Data Cleaningor Data Wrangling. • Exploratory Data Analysis (EDA) plays an important role at this stage as summarization or clean datahelps in identifying the structure, outliers, anomalies and patterns in the data Data Science Life Cycle Data Science Applications • Fraud and Risk Detection. • Healthcare. • Internet Search • Targeted Advertsing. • Website Recommendations • Advanced Image Recogniton. • Speech Recognition • Airline Route Planning

Foundation Course on Information Technology Outsourcing 11.3 Basics of Data Analytics Data analytics is the process of collecting data in raw form, processing is based on the needs of the user and utilizing it for decision-making purposes. Data analysts and data scientists perform data analysis. This process involves data cleaning, inspection, transformation, modeling to understand data from its raw form. Business intelligence needs data analytics to perform its operations. A large amount of data is unstructured and it has to be collected from various sources. • Data analytics helps to acquire problem-solving skills in any type of business as it is used by many professionals and students. This helps to approach the problems analytically and solve them in the most logical way. This helps the user in daily life as well. • The skill is in high demand right now as there is skills’ shortage globally. Learning basics is the starting point of learning data science and hence machine learning. • Data is available everywhere and it has become the need of the hour to know how to analyze the data in our hand and to understand how our data is being analyzed for various businesses. Types of Data Analytics Data analytics is a broad field. There are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and a different place in the data analysis process. These are also the primary data analytics applications in business. • Descriptive analytics helps answer questions about what happened. These techniques summarize large datasets to describe outcomes to stakeholders. By developing key performance indicators (KPIs,) these strategies can help track successes or failures. Metrics such as return on investment (ROI) are used in many industries. Specialized metrics are developed to track performance in specific industries. This process requires the collection of relevant data, processing of the data, data analysis and data visualization. This process provides essential insight into past performance. • Diagnostic analytics helps answer questions about why things happened. These techniques supplement more basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why they got better or worse. This generally occurs in three steps: ➢ Identify anomalies in the data. These may be unexpected changes in a metric or a particular market.

Foundation Course on Information Technology Outsourcing ➢ Data that is related to these anomalies is collected. ➢ Statistical techniques are used to find relationships and trends that explain these anomalies. • Predictive analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future and their techniques include a variety of statistical and machine learning techniques, such as neural networks, decision trees, and regression. • Prescriptive analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven decisions can be made. This allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large datasets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated. Applications of Data Analytics Basics • It helps in health care to know various causesof different diseases and to analyze the data to help the patients understand the risk involved and to recognize the disease easily. This helps in giving proper treatment to the patients. • Database analytics helps to do the internet search as various data helps to collect information in searching the data. • The analytics helps in determining the images and doing speech recognition. Clustering the images and determining their groups is one of the applications of Data analytics. • Data analytics is easy to learn and can be mastered by anyone with the basics of computer programming and analytics. This helps the users to grasp more about data. • Data analytics helps in recommending the websites to the clients and this is helpful to the users who do data analytics in a perfect manner. 11.4 Summary • Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis.

Foundation Course on Information Technology Outsourcing • Data science can be used to gain knowledge about behaviors and processes, write algorithms that process large amounts of information quickly and efficiently, increase security and privacy of sensitive data, and guide data-driven decision-making • Data analytics helps individuals and organizations make sense of data. • Data analytics helps individuals and organizations make sense of data. • Data analytics use various tools and techniques to help organizations make decisions and succeed. 11.5 Glossary • Fuzzy Algorithms: Algorithms that use fuzzy logic to decrease the runtime of a script. Fuzzy algorithms tend to be less precise than those that use Boolean logic. • Data Analysis: This discipline is the little brother of data science. Data analysis is focused more on answering questions about the present and the past. • Data Wrangling: The process of taking data in its original form • Data Science: discipline of using data and advanced statistics to make prediction 11.6 References • https://www.crcpress.com/Introduction-to-Data-Science-Data-Analysis-and- Prediction-Algorithms-with/Irizarry/p/book/9780367357986↩︎ • https://leanpub.com/datasciencebook • https://github.com/rafalab/dsbook • https://www.scribd.com/document/178275229/Introduction-to-Data-Science

Foundation Course in Information Technology Outsourcing: Introduction to Analytics 11.4 Summary • Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. • Data science can be used to gain knowledge about behaviours and processes, write algorithms that process large amounts of information quickly and efficiently, increase security and privacy of sensitive data, and guide data-driven decision-making • Data analytics helps individuals and organizations make sense of data. • Data analytics helps individuals and organizations make sense of data. • Data analytics use various tools and techniques to help organizations make decisions and succeed. 11.5 Glossary • Fuzzy Algorithms: Algorithms that use fuzzy logic to decrease the runtime of a script. Fuzzy algorithms tend to be less precise than those that use Boolean logic. • Data Analysis: This discipline is the little brother of data science. Data analysis is focused more on answering questions about the present and the past. • Data Wrangling: The process of taking data in its original form • Data Science: The discipline of using data and advanced statistics to make a prediction 11.6 References • https://www.crcpress.com/Introduction-to-Data-Science-Data-Analysis-and- Prediction-Algorithms-with/Irizarry/p/book/9780367357986↩︎ • https://leanpub.com/datasciencebook • https://github.com/rafalab/dsbook • https://www.scribd.com/document/178275229/Introduction-to-Data-Science Page 7 of 7 All Rights Reserved. Vol. TLE001/03-2022