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6 Phases of Data Analytics Lifecycle Every Data Analyst Should Know About

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6 Phases of Data Analytics Lifecycle

Are you an aspiring data analyst looking to take up a data analytics course in Pune? Then, learning about the data analytics lifecycle is fundamental to your knowledge and expertise. So, let’s look at the data analytics lifecycle and the six phases, including discovery, data preparation, model planning, model building, communication results, and operationalization that make up for it. Data Analytics Lifecycle and its Significance The data analytics lifecycle defines the roadmap of the way data is generated, collected, processed, used, and analyzed to accomplish business goals. These processes refer to an organized way of converting data into useful information to help businesses achieve project or organizational goals. The lifecycle guides and provides strategies for extracting information and moving in the right direction to achieve business objectives. Analysts use the circular representation of the lifecycle to analyze data in a forward or backward direction. The insights they receive help them decide whether to proceed with the existing research and stop or rework the analysis. Why should you learn about the data analytics lifecycle? The lifecycle aims to address big data problems and data science projects. The systematic and step-by-step methodology helps analysts plan tasks concerning data acquisition, processing, analysis, and recycling. These phases or stages help data analysts address specific big data analysis needs. 6 Stages of Data Analytics Process Before we see the phases of data analytics, let’s look at the various steps involved in data analysis with an example. Let’s say, an eCommerce portal is struggling with a massive number of cart abandonments. The decision-makers have taken cognizance of this concern and want to know what’s driving people away from the brand after creating a cart. As a data analyst, this is what you would do. 1. Define the Problem The process begins with understanding the task and the stakeholder’s expectations for the solution. It would involve asking the managers and other stakeholders questions about cart abandonments to find a solution to their problem. It would also involve finding the problem’s root cause to understand the concern. A couple of key questions that you must ask yourself include; Which problems have the stakeholders mentioned What are their expectations from the solution 2. Data Collection The next step is collecting data from multiple sources, including external and internal. Internal data is available in the company, whereas external information will have to be collected from outside the organization. Data generated from own resources is first-party data, while that collected and sold is called second-party data. On the other hand, data collected from external sources is termed third-party data. Common sources of data are feedback, questionnaires, surveys, etc. Accordingly, as a data analyst, you will have to collect cart abandonment data from the system and conduct online surveys to ask users why did they abandon the cart. 3. Data Cleanup The next process is cleaning the data collected. It might comprise redundancies, duplication, and irrelevant information. You must remove such data to ensure you have relevant and only the data you need to analyze. While helping you analyze the data effectively, it would also enable you to identify trends and patterns. Another significant part of this process is determining if the data is biased toward something. Such data wouldn’t let you drive the right inferences. 4. Data Analysis This is where the actual analysis begins. It involves analyzing the data, identifying trends, making calculations (using tools like Excel or SQL (Structured Query Language), and combining data for better outcomes. Additionally, programming languages like R and Python also help you analyze data. In the context of the eCommerce company, it would involve understanding, analyzing, and grouping the various reasons for cart abandonments. Check Out Full Blog – Data Preprocessing in Machine Learning: 7 Easy Steps to Follow 5. Data Visualization Visualizing helps non-technical people or the consumers of the data understand complex data. The transformed data has to be made into a visual, including a chart or a graph for a simpler comprehension of the data. You can leverage various tools to do that. A couple of them include Tableau and Looker. Tableau includes a simple drag-and-drop tool that helps create effective visualizations. Whereas, Looker is a data viz tool directly connecting to the database and creating visualizations.  6. Data Presentation Presentation is the last step in the data analysis process. It involves transforming raw information into an easily comprehensible and meaningful format. You can present the data in various forms, including graphs, charts, tables, etc., to make it easier for decision-makers to draw conclusions and make informed decisions. For example, after analyzing the data, you’ve categorized various reasons for cart abandonments, including slowly loading web pages, external distractions, network issues, unspecified, etc. If you decide to show it through a pie chart, you will be able to show the reasons and their pie share depending on the cart cancellations they result in. If slowly loading the webpage is the most common reason, the company can make efforts to enhance the website’s speed and gradually reduce the number of canceled carts. Data Analytics Lifecycle Phases Here are the six phases that form the data analytics lifecycle. Phase 1: Discovery The data science team explores the issue and investigates it. It builds context and understanding. Learn about the required and available data sources. The team builds an initial hypothesis that can later be tested with data. Phase 2: Data Preparation Methods or steps to discover, preprocess, and condition data before modeling and analysis. An analytic sandbox is required. The team executes, loads, and transforms to get data into the sandbox. The team may perform data preparation tasks several times and not in a predefined order. Some tools used for this phase are Alpine Miner, Hadoop, and Open Refine. Phase 3: Model Planning The data science team studies the data to identify connections between variables. Next, it selects crucial variables and the most useful models. Datasets used for testing, production, and training goals are created. The team builds and executes models depending on … Read more

Why Data Analytics Courses in Pune Stand Out: An Expert Review

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Data Analytics

Data analytics is changing quickly. Pune has good data analytics institutes. In Pune, institutes offer data analytics courses. It’s hard to choose the best one. This review will show what makes data analytics courses in Pune unique. It examines the course curriculum, faculty, practice sessions, and placement services. Why Choose Ethan’s Tech? Ethan’s Tech offers data analytics courses in Pune. This makes them unique. Ethan’s Tech is proud of its education quality and student success. They also consistently get great results. They provide an excellent training program. It covers basic and advanced analytics. Ethan’s Tech has a team of skilled experts. They teach both theory and real-life examples. The course includes assignments, case studies, and live projects. This helps students understand the subject well. They can work with actual data issues. The program has hands-on projects. This gives students useful job skills. This practical approach shows how data analytics is done. Ethan’s Tech helps students with resumes and interviews. This support prevents students from struggling to find good jobs. The institute offers both online and offline classes. Students can choose the option that works best for them. Ethan’s Tech has a strong placement program. It helps students get jobs at reputable companies. The program stays up-to-date with industry needs. This gives students many chances to interview and find good jobs in analytics and related fields. Check Out : Future-Proof Your Career with Data Analytics Training in Pune Placement Success Ethan’s Tech includes outstanding placement assistance which is among other key strengths of this company. The institution has a placement office that assists students in getting placements in reputed companies. Industry relevance is well established so students get many chances to interview and get placed in data analytics and related domains.  IIM SKILLS IIM Skills has live classes, projects, and support in its data analytics course. It is for beginners and professionals who want to improve their skills. IIM SKILLS focuses on providing both academic knowledge and practical skills. Their live classes are organized well and let students work on projects. The 24/7 support system helps students get help anytime, making learning more flexible. Udemy There are many data analytics courses on Udemy, a popular e-learning website. These courses are flexible. This means students can study on their own schedule. Experts in the field create these courses. They cover topics like statistics and machine learning. Udemy often has sales and discounts. This makes most of these courses affordable. Many learners can access them. DataCamp DataCamp offers data science and data analytics courses. These make learning data analytics enjoyable. The platform has lots of helpful content and practical lessons. It doesn’t have strict rules, so it’s perfect for learners wanting to specialize in data. DataCamp also has a coding section. Here, students do coding exercises and get feedback. This helps them remember what they learn better. Simplilearn Simplilearn helps people learn new skills. It offers online courses. These courses teach useful skills. Simplilearn makes it easy to learn. Simplilearn offers coaching, classes, projects, and certifications in data analytics. Their courses help beginners and those wanting to advance their data analytics careers. Simplilearn’s certifications provide both learning and job opportunities. This can improve your career prospects. The classes and projects give students the knowledge they need. This preparation helps them meet the standard requirements of industries. Coursera Coursera is an online learning platform. It offers courses from universities and companies. People can take these courses online. Some data analytics classes are on Coursera. Professionals make them. Learners at different levels can take them. The content is current and follows industry best practices. Coursera lets students study at their own pace. Conclusion Pune has many good data analytics courses. Each course has its features. The course content, trainer profile, and placement facilities are important. Ethan’s Tech stands out in these areas. Any of these top courses will help students gain the needed knowledge and skills for data analytics in today’s world.

Differences Between Data Analytics vs Data Analysis

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What is Data Analysis | Ethan's Tech

In today’s data-driven world, organizations are constantly seeking insights to make informed decisions and gain a competitive edge. Data analytics and data analysis are two terms that are often used interchangeably, but they have distinct meanings and purposes. This article aims to clarify the differences between data analytics and data analysis, highlighting their unique characteristics and applications. Definition and Scope Data analysis involves examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It focuses on extracting insights and patterns from raw data to understand trends, relationships, and correlations. On the other hand, data analytics is a broader field that encompasses the entire process of collecting, organizing, interpreting, and communicating data to drive business strategies and actions. Key Objectives The primary objective of data analysis is to uncover meaningful patterns, identify outliers, and gain a deep understanding of the data. It aims to answer specific questions and solve defined problems using statistical and mathematical techniques. Data analytics, on the other hand, focuses on extracting actionable insights from data to guide strategic decision-making, optimize processes, and improve overall business performance. Methodologies and Techniques Data analysis employs various methodologies and techniques, including statistical analysis, data mining, predictive modeling, and exploratory data analysis. It relies on statistical tools and software to analyze data sets and generate descriptive and inferential statistics. Data analytics encompasses a wider range of methodologies, including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. It utilizes advanced algorithms and machine learning techniques to analyze complex data sets and make data-driven predictions and recommendations. Tools and Technologies Both data analysis and data analytics utilize a combination of tools and technologies to process and analyze data effectively. Common tools for data analysis include spreadsheet software like Microsoft Excel, statistical software such as R and SAS, and programming languages like Python. Data analytics, on the other hand, leverages a broader range of tools, including business intelligence platforms, data visualization software, big data frameworks like Hadoop, and cloud-based analytics solutions. Skill Requirements Professionals in data analysis typically possess strong statistical and mathematical skills. They are proficient in programming languages and have a deep understanding of statistical techniques and models. Data analysts are skilled in data cleaning, data manipulation, and data visualization. In contrast, data analytics professionals require a broader skill set. They need a solid foundation in statistics and programming but also need business acumen and communication skills to translate complex data insights into actionable recommendations for business stakeholders. Applications and Industries Data analysis finds applications in various fields, including finance, marketing, healthcare, and social sciences. It is used to detect fraud, conduct market research, perform risk assessments, and analyze survey data. Data analytics, with its focus on strategic decision-making, is widely used in industries such as e-commerce, supply chain management, customer relationship management, and cybersecurity. It helps businesses optimize their operations, personalize customer experiences, forecast demand, and enhance security measures. Data Analytics and Data Analysis in Business In a business context, data analysis is often employed to gain insights into historical data and understand past performance. It enables businesses to evaluate the effectiveness of marketing campaigns, assess customer satisfaction, and identify areas for improvement. Data analytics, on the other hand, enables organizations to take a proactive approach by leveraging real-time and predictive analytics. It helps businesses anticipate trends, detect anomalies, and make data-driven decisions to stay competitive in dynamic markets. Challenges and Limitations Both data analysis and data analytics face certain challenges and limitations. Data analysis can be time-consuming, especially when dealing with large data sets. It requires expertise in statistical analysis and may be limited by the quality and availability of data. Data analytics, while powerful, relies on accurate and relevant data to generate meaningful insights. Organizations need to invest in data governance and ensure data quality to derive accurate and actionable insights. Future Trends The field of data analytics and data analysis is evolving rapidly, driven by advancements in technology and the increasing availability of data. Future trends include the integration of artificial intelligence and machine learning algorithms into data analytics processes, the rise of real-time analytics for instant decision-making, and the growing importance of data privacy and ethics in data-driven practices. Conclusion In conclusion, while data analytics and data analysis are related disciplines, they have distinct characteristics and objectives. Data analysis focuses on examining and interpreting data to uncover patterns and insights, while data analytics encompasses the entire process of data collection, analysis, and strategic decision-making. Both fields are essential for organizations to harness the power of data and gain a competitive advantage in today’s fast-paced business environment.