INTRO TO DATA ANALYSIS - Axia Africa PDF

INTRO TO DATA ANALYSIS  Agenda Introduction Skills Processes Applications Career Opportunities 2 Data And Its Sources I know you’ll be tempted to ask what data analytics is exactly. I will start by explaining what data is. W...

INTRO TO DATA ANALYSIS  Agenda Introduction Skills Processes Applications Career Opportunities 2 Data And Its Sources I know you’ll be tempted to ask what data analytics is exactly. I will start by explaining what data is. What is Data? Data refers to raw, unprocessed information like numbers, text, images, or videos. It serves as the foundation for identifying patterns, drawing insights, and making informed decisions. 3 TYPES OF DATA Definition: A data type is a collection or grouping of information, value items to a specified possible set. Quantitative (Numerical) Data: On the The types of Data are: Qualitative and Quantitative Data Qualitative (Categorical) Data: other hand, quantitative data deals with Qualitative data are data used to describe a numbers or numeric values on which we can group of items or an object. It’s also known apply mathematical operations. For example, as categorical data because, it is used to height, fruits in a basket, kids in a school, label or categorize a group of items or data items in a stop etc. points to a specific form. Examples include Although they seem similar, here’s something colors, plants, and places, clothes, else to keep in mind – quantitative data can countries, races, e.t.c. However qualitative be continuous or discrete. Quantitative or data can further be classified into two numerical data can be further split into subtypes, namely: Ordinal and Nominal. continuous and discrete data. The difference ORDINAL DATA: Ordinal data are those in the two is that continuous data can be data that follow a specific order or ranking, reduced or divided further into smaller units as in test grades, economic status, or e.g weight. The weight of an object can be military rank, e.t.c. divided into smaller units like grams, NOMINAL DATA: Nominal data, however, doesn’t follow a specific order like ordinal milligrams, etc and get meaningful values 4 data. Consider gender, city, employment afterwards while discrete data cannot be  The diagram below explains better all that we have said. However, in python there are several other data types that can be used to execute a function. Example strings, integers, Boolean, tuple, list, dictionary, etc. 5  DATA COLLECTION/SOURCING Data collection is the process of gathering, measuring, and recording data or information for research, analysis or decision making. It can simply be put as the process of gathering raw data from various sources to answer relevant questions. There are various reasons why data collection is relevant, some of such reasons include:  It helps to discover trends in the way people or items change both in opinions and behavior over time at different circumstances.  It facilitates decision making and improves the quality of decisions made.  Its helps to improve the quality of products or services and in resolving issues.  Its helps to understand the target market and the best strategy to use 6 in marketing products/services.  Types and Method of Data Collection There are two main ways of data collection to include: Primary and Secondary data collection. METHODS OF COLLECTING DATA PRIMARY DATA COLLECTION: This is also referred There are about five tools for data to as raw data collection. It is the data that is collected collection: first hand from the source. This suggests that the data Surveys, Quizzes and is unstructured, unorganized and nothing meaningful Questionnaires has been done with it thus makes it a bit more difficult to analyze compared to the secondary data. Interviews SECONDARY DATA: This refers to information that has been collected, structured, and analyzed by Focus groups. another person. It is easier to analyze because some Direct observation work has been done on it initially and as such makes working on it faster however getting a secondary data Documents and records from that perfectly suits a situation can be difficult. sources like the internet, database, archive, etc. 7  Data Analytics Data in its raw form doesn’t make complete sense and now, the process of preparing this data and getting it ready for use is known as data analytics. What is Data Analytics? What is Data Analysis? What is a Data Science? Data analytics is the process of collecting, organizing, and Data analysis is a subset of Data science is a broader field analyzing data to find patterns data analytics that focuses on that uses scientific methods to and insights. interpreting the results of data extract insights from data. It A data analyst is a analysis to make informed combines aspects of data decisions. Data analysts and analytics, statistics, and professional who collects, data scientists often work machine learning. processes, and interprets data together to make sense of to uncover insights that help data. organizations make informed decisions. Data analysts use tools like Excel, Tableau, and SQL to extract meaningful 8 information from data.  Types of Data Analytics Descriptive Analytics: This has to do with insights gotten from historic data from past events. (What happened) It provides insights into what happened in the past by analyzing historical data. Descriptive analytics helps organizations understand the current state of affairs and identify trends, patterns, and relationships in their data. It is often used for reporting and dashboards to help decision-makers monitor key performance indicators (KPIs) and assess performance. Diagnostic Analytics: Identifying the reasons behind past events or data (Why it happened). An example is a decline in sales in a particular region. Predictive Analytics: This is used to make predictions about future events based on historical data (What might happen in future). An example of predictive analytics would be using past football records to determine the winner of World cup. Another example is an ad on Facebook/tweets which is filtered based on your known interests and behaviour. Prescriptive Analytics: Prescriptive analytics provides decision-makers with specific actions to take to achieve a desired outcome (How can we improve). An example is a healthcare provider that uses predictive analytics to identify patients at risk of developing a chronic 9 condition and prescribes personalized interventions to prevent or manage the condition.  What Skills do I need to have as a Data Analyst Technical Skills: Soft Skills: Business ⚬ Excel understanding ⚬ Power Analytical thinking BI/Tableau Problem-solving ⚬ SQL Communication ⚬ Python Teamwork Storytelling There are various stacks or tools used for carrying out this data analytics. Excel, Power BI, Tableau, Google Data Studio, Excel, Python, SQL etc. 10  Data Analytics Process Problem statement and Objectives Data Extraction There are 5 key steps of carrying out data Data Cleaning analytics; Problem Understanding: It is your job as a Data Analysis data analyst to understand the business problem. Data Visualization Data Extraction: This is also known as data collection/sourcing. Data Cleaning: Clean and prepare the data On a final note: Data Analyst present for analysis. the: Analyze the data Interpretation/Insights Data visualization: Using pictures, charts Recommendation and graphs to interpret the results of the data. 11 Applications of Data Analytics Data analytics is a rapidly growing field and almost every facet of life is using data analytics techniques to extract insights and knowledge from large and complex datasets. Among the most significant uses of data analytics are:.  Marketing & Retail: Data analytics is widely used in business intelligence to help organizations make data- driven decisions. By analyzing large amounts of data, businesses can gain valuable insights into their operations, customers, and market trends, which can help them optimize their processes and increase their revenue. Targeting the right audience and optimizing advertising strategies.  Finance: Detecting fraud, managing risks, and forecasting market trends.  Healthcare: Enhancing patient care, predicting disease outbreaks, and improving treatment outcomes.  Sports: Evaluating player performance and developing winning strategies. 12  Application of Data Analytics (Cont’d)  Social Media: Analyzing user behavior to improve engagement and user experience.  Transportation: Data analytics is used extensively in the transportation industry to optimize routes and reduce costs. By analyzing data on traffic patterns and transportation routes, transportation companies can identify the most efficient routes and make real-time adjustments to optimize their operations.  Education: Data analytics is used extensively in education to improve student outcomes and personalize learning. By analyzing student data, educators can identify patterns and trends that can help them develop targeted interventions to improve student performance, as well as personalize learning experiences to meet the needs of individual students. 13 Career Opportunities in Data 1. Data Analyst 2. Business Intelligence Analyst 3. Data Scientist 4. Data Engineer 5. Machine Learning Engineer 6. Data Visualization Specialist 7. Product Data Analyst 8. Database Administrator 9. Marketing Analyst 10. Operations Analyst 11. Statistical Analyst 12. Big Data Analyst 13. Risk Analyst 14. Healthcare Data Analyst 15. Financial Data Analyst 16. Social Media Analyst 17. Supply Chain Analyst 18. Fraud Analyst 19. Customer Insights Analyst 14 20. Data Consultant Thank you Ifeoluwa