Introduction to Data Science PDF

Basics of Data What is Data ? Data is a word we hear everywhere nowadays. In general, data is a collection of facts, information, and statistics and this can be in various forms such as numbers, text, sound, images, or any other format. According to Oxford “Data is distinct pieces of information, us...

Basics of Data What is Data ? Data is a word we hear everywhere nowadays. In general, data is a collection of facts, information, and statistics and this can be in various forms such as numbers, text, sound, images, or any other format. According to Oxford “Data is distinct pieces of information, usually formatted in a special way”. Data can be measured, collected, reported, and analyzed, whereupon it is often visualized using graphs, images, or other analysis tools. Raw data (“unprocessed data”) may be a collection of numbers or characters before it’s been “cleaned” and corrected by researchers. It must be corrected so that we can remove outliers, instruments, or data entry errors. Data processing commonly occurs in stages,and therefore the “processed data” from one stage could also be considered the “raw data” of subsequent stages. Field data is data that’s collected in an uncontrolled “in situ” environment. Experimental data is the data that is generated within the observation of scientific investigations. Data can be generated by: ->Humans ->Machines ->Human-Machine combined. It can often be generated anywhere where any information is generated and stored in structured or unstructured formats. What is Information ? Information is data that has been processed , organized, or structured in a way that makes it meaningful, valuable and useful. It is data that has been given context , relevance and purpose. It gives knowledge, understanding and insights that can be used for decision-making , problem-solving,communication and various other purposes. Why is data important ? ->Data helps in making better decisions. ->Data helps in solving problems by finding the reason for underperformance. ->Data helps one to evaluate the performance. ->Data helps one improve processes. ->Data helps one understand consumers and the market. Types of data: Structured data is data that has a standardized format for efficient access by software and humans alike. It is typically tabular with rows and columns that clearly define data attributes. Computers can effectively process structured data for insights due to its quantitative nature. Semi-structured data refers to data that is not captured or formatted in conventional ways. Semi-structured data does not follow the format of a tabular data model or relational databases because it does not have a fixed schema. Unstructured data are datasets that have not been structured in a predefined manner. Unstructured data is typically textual, like open-ended survey responses and social media conversations, but can also be non-textual, like images, video, and audio. Why is data science important? Data science is important because it combines tools, methods, and technology to generate meaning from data. Modern organizations are inundated with data; there is a proliferation of devices that can automatically collect and store information. Online systems and payment portals capture more data in the fields of e-commerce, medicine, finance, and every other aspect of human life. We have text, audio, video, and image data available in vast quantities. Future of data science Artificial intelligence and machine learning innovations have made data processing faster and more efficient. Industry demand has created an ecosystem of courses, degrees, and job positions within the field of data science. Because of the cross-functional skills and expertise required, data science shows strong projected growth over the coming decades. What is Data Science? Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results. Data Science Process 1. Problem Definition Churn rate is the percentage of customers who stop using a product or service over a given period. For many businesses, especially subscription-based services, reducing churn is crucial for sustaining revenue and growth. Example: A telecommunications company has noticed an increasing number of customers canceling their service. They want to identify factors that lead to churn and predict which customers are most likely to leave in the near future, so they can intervene and retain these customers. 2. Data Collection To understand and predict churn, you need data on customer behavior, demographics, and past interactions with the company. Collect data from various sources: ->Customer Demographics: Age, location, subscription plan. ->Usage Data: Frequency of service use, duration of use. ->Interaction Data: Customer service calls, complaints, feedback. ->Historical Churn Data: Records of past churned customers and their characteristics 3. Data Preparation Raw data often contains errors, missing values, or inconsistencies. Preparing data involves cleaning and transforming it into a usable format. 4. Exploratory Data Analysis (EDA) EDA involves visualizing and summarizing data to uncover insights and guide further analysis. Example: Visualization: Use histograms to show distribution of churn rates among different customer segments, scatter plots to visualize relationships between usage frequency and churn, and box plots to explore churn across different subscription plans. 5. Model Building Various algorithms can be used to build a model that predicts whether a customer will churn. Example: Algorithm Selection: Choose algorithms such as logistic regression, decision trees, random forests, or gradient boosting machines based on their suitability for classification tasks. Training: Use historical data where the churn outcome is known to train the model. Split data into training and validation sets to assess performance. 6. Model Evaluation Evaluating the model helps ensure it performs well and meets business needs. Example: Metrics: Evaluate using metrics such as accuracy, precision, recall, F1 score, and ROC curve. For churn prediction, recall (sensitivity) is crucial because identifying as many potential churners as possible is important. 7. Model Deployment The model should be integrated into systems where it can impact business processes, such as marketing or customer service. Example: Integration: Deploy the model into the company’s CRM system to score customers and flag those at high risk of churn. Automation: Set up automated workflows to trigger retention offers or customer outreach based on model predictions. 8. Monitoring and Maintenance Models may degrade over time due to changes in data patterns. Real-world Applications of Data Science 1.In Transport Data Science is also entered in real-time such as the Transport field like Driverless Cars. With the help of Driverless Cars, it is easy to reduce the number of Accidents. For Example, In Driverless Cars the training data is fed into the algorithm and with the help of Data Science techniques, the Data is analyzed like what as the speed limit in highways, Busy Streets, Narrow Roads, etc. And how to handle different situations while driving etc. 2. In Finance Data Science plays a key role in Financial Industries. Financial Industries always have an issue of fraud and risk of losses. Thus, Financial Industries needs to automate risk of loss analysis in order to carry out strategic decisions for the company. Also, Financial Industries uses Data Science Analytics tools in order to predict the future. It allows the companies to predict customer lifetime value and their stock market moves. For Example, In Stock Market, Data Science is the main part. In the Stock Market, Data Science is used to examine past behavior with past data and their goal is to examine the future outcome. Data is analyzed in such a way that it makes it possible to predict future stock prices over a set timetable. 3. In E-Commerce E-Commerce Websites like Amazon, Flipkart, etc. uses data Science to make a better user experience with personalized recommendations. For Example, When we search for something on the E-commerce websites we get suggestions similar to choices according to our past data and also we get recommendations according to most buy the product, most rated, most searched, etc. This is all done with the help of Data Science. 4. In Health Care In the Healthcare Industry data science act as a boon. Data Science is used for: Detecting Tumor. Drug discoveries. Medical Image Analysis. Virtual Medical Bots. Genetics and Genomics. Predictive Modeling for Diagnosis etc. 5. Image Recognition Currently, Data Science is also used in Image Recognition. For Example, When we upload our image with our friend on Facebook, Facebook gives suggestions Tagging who is in the picture. This is done with the help of machine learning and Data Science. When an Image is Recognized, the data analysis is done on one’s Facebook friends and after analysis, if the faces which are present in the picture matched with someone else profile then Facebook suggests us auto-tagging. 6. Targeting Recommendation Targeting Recommendation is the most important application of Data Science. Whatever the user searches on the Internet, he/she will see numerous posts everywhere. This can be explained properly with an example: Suppose I want a mobile phone, so I just Google search it and after that, I changed my mind to buy offline. In Real -World Data Science helps those companies who are paying for Advertisements for their mobile. So everywhere on the internet in the social media, in the websites, in the apps everywhere I will see the recommendation of that mobile phone which I searched for. So this will force me to buy online. 7. Airline Routing Planning With the help of Data Science, Airline Sector is also growing like with the help of it, it becomes easy to predict flight delays. It also helps to decide whether to directly land into the destination or take a halt in between like a flight can have a direct route from Delhi to the U.S.A or it can halt in between after that reach at the destination. 8. Data Science in Gaming In most of the games where a user will play with an opponent i.e. a Computer Opponent, data science concepts are used with machine learning where with the help of past data the Computers will improve their performance. There are many games like Chess, EA Sports, etc. that use Data Science concepts. 9. Medicine and Drug Development The process of creating medicine is very difficult and time-consuming and has to be done with full disciplined because it is a matter of Someone’s life. Without Data Science, it takes lots of time, resources, and finance or developing new Medicine or drug but with the help of Data Science, it becomes easy because the prediction of success rate can be easily determined based on biological data or factors. The algorithms based on data science will forecast how this will react to the human body without lab experiments. 10. In Delivery Logistics Various Logistics companies like DHL, FedEx, etc. make use of Data Science. Data Science helps these companies to find the best route for the Shipment of their Products, the best time suited for delivery, the best mode of transport to reach the destination, etc. What is data analytics? Data analytics is the process of analyzing raw data in order to draw out meaningful, actionable insights, which are then used to inform and drive smart business decisions. Types of data analytics 1. Descriptive analysis Descriptive analysis examines data to gain insights into what happened or what is happening in the data environment. It is characterized by data visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives. For example, a flight booking service may record data like the number of tickets booked each day. Descriptive analysis will reveal booking spikes, booking slumps, and high-performing months for this service. 2. Predictive analysis Predictive analysis uses historical data to make accurate forecasts about data patterns that may occur in the future. It is characterized by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. In each of these techniques, computers are trained to reverse engineer causality connections in the data. For example, the flight service team might use data science to predict flight booking patterns for the coming year at the start of each year. The computer program or algorithm may look at past data and predict booking spikes for certain destinations in May. Having anticipated their customer’s future travel requirements, the company could start targeted advertising for those cities from February. 3. Prescriptive analysis Prescriptive analytics takes predictive data to the next level. It not only predicts what is likely to happen but also suggests an optimum response to that outcome. It can analyze the potential implications of different choices and recommend the best course of action. It uses graph analysis, simulation, complex event processing, neural networks, and recommendation engines from machine learning. Back to the flight booking example, prescriptive analysis could look at historical marketing campaigns to maximize the advantage of the upcoming booking spike. A data scientist could project booking outcomes for different levels of marketing spend on various marketing channels. These data forecasts would give the flight booking company greater confidence in their marketing decisions.