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Business Analyst Full Course [2024] | Business Analyst Tutorial For Beginners | Edureka

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Business analysts play a crucial role in bridging technology and business, with high demand for their skills in current job market. The Edureka Business Analytics course covers essential topics like predictive analysis, technical concepts, agile methodologies, and job opportunities for aspiring business analysts.

Insights

Business analysts play a crucial role by bridging technology and business, requiring a mix of technical and business skills.
The Edureka Business Analytics video offers a comprehensive understanding of business analytics, covering trends, technical concepts, methodologies, and interview questions.
Job opportunities for business analysts are abundant globally, with varying salaries and positions available, especially in companies like IBM, Deloitte, and Oracle.
Essential skills for business analysts include SQL proficiency, analytical skills, project management, and data visualization tools like Tableau or Power BI.
Predictive Analytics involves using statistical techniques to predict future events based on data, with applications in various fields like marketing, finance, and fraud detection.
Agile methodologies like Scrum emphasize iterative development, collaboration, and rapid deployment, contrasting with traditional models like the Waterfall approach.

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Recent questions

What skills are essential for a business analyst?
SQL proficiency, Microsoft tools knowledge, analytical skills, basic programming, statistical analysis, project management, and Scrum methodology are crucial for business analysts.
What are the steps in predictive analysis?
Data exploration, data cleaning, modeling, and performance analysis are the key steps in predictive analysis.
What are the applications of predictive analysis?
Predictive analysis is used in campaign management, customer acquisition, budgeting, forecasting, stock prediction, fraud detection, promotions, and pricing.
What are the responsibilities of a business analyst?
Business analysts are responsible for data collection, review, validation, gathering end-user requirements, and maintaining customer data.
What are some common BI tools used by companies?
Companies utilize BI tools like SAP Business Objects, MicroStrategy, SAS BI Intelligence, Qlik Sense, Zoho Analytics, and Power BI for business intelligence needs.

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Summary

00:00

"High Demand for Business Analysts Worldwide"

Business analysts bridge the gap between technology and business, in high demand due to their mix of technical and business skills.
The Edureka Business Analytics full course video aims to provide a comprehensive understanding of business analytics from theory to practical applications.
The video covers current trends, becoming a business analyst, the role of a business analyst, technical concepts like predictive analysis using Python and data analytics using Excel.
It also delves into agile and scrum methodologies, becoming a BI analyst, and offers insights into important business analytics interview questions.
Job opportunities for business analysts are abundant, with around 13,000 vacant jobs in India and 100,000 in the U.S., including entry-level and experienced positions.
Salaries for business analysts range from 7 lakhs per year in India to 77,000 per year in the U.S., based on data from LinkedIn and Glassdoor.
Companies hiring business analysts include IBM, Deloitte, Capgemini, Oracle, Accenture, and various startups, offering opportunities at different levels.
Job descriptions from companies like Amazon, IBM, and Oracle highlight the skills required for business analysts, such as SQL, Microsoft Excel, data visualization tools, and statistical analysis.
Essential skills for becoming a business analyst include SQL proficiency, knowledge of Microsoft tools, analytical skills, basic programming knowledge, statistical analysis, project management, and Scrum methodology.
Business analysts use data visualization tools like Tableau or Power BI and must focus on customer-centric approaches, visual thinking, data-driven planning, and cybersecurity.

18:27

"Predictive Analytics in Project Management and Python"

Stuart conducts meetings to address mistakes and issues in the project, ensuring everyone is informed.
Martha stays updated on the project's progress through these meetings.
After hard work, a prototype is developed and user testing commences.
Feedback from testing aids in improving and finalizing the product.
The app's performance is analyzed using data visualization tools like Tableau and Power BI.
Reports are generated to provide insights on the app's performance.
Stuart documents all project information, including app documentation and findings, for presentation to stakeholders.
The hospital Health app is delivered on time and within budget, pleasing Martha, the CTO, and the hospital.
Predictive Analytics involves using statistical techniques to predict future events based on current and historical data.
Applications of predictive analysis include campaign management, customer acquisition, budgeting, forecasting, stock prediction, fraud detection, promotions, and pricing.
Steps in predictive analysis include data exploration, data cleaning, modeling, and performance analysis.
Data exploration involves understanding the data's structure and features.
Data cleaning eliminates redundancies like missing values and outliers.
Modeling selects the appropriate predictive model, like linear regression.
Performance analysis evaluates the model's accuracy, aiming for over 70%.
Python is used to perform predictive analysis on a dataset, predicting house prices.
Dependencies like pandas, seaborn, and numpy are imported for data analysis.
The dataset's columns and shape are examined to understand the data structure.

33:14

"Data Analysis: House Prices and Linear Regression"

Kaggle is a platform where various datasets can be found and downloaded for analysis.
The "describe" method in data analysis provides statistical information like mean, minimum, maximum, and standard deviation for numerical values.
The dataset being explored includes information on bedrooms, bathrooms, and square footage of houses.
The mean number of bedrooms in the dataset is three, with a common entry being a three-bedroom house.
The dataset includes a house with 33 bedrooms, eight bathrooms, and 13,540 square feet.
The minimum values in the dataset include a zero-bedroom house and 290 square feet.
Data visualization is used to understand relationships between variables in the dataset.
Checking for null values is crucial before modeling to ensure data cleanliness.
The process of data cleaning involves removing redundancies and unnecessary columns for accurate modeling.
Linear regression is used for modeling house prices based on various factors like bedrooms, bathrooms, and square footage.

50:41

Estimating Variance and Analyzing Relationships in Excel

Standard deviation is used to estimate variance in a sample, denoted as standard deviation dot s, only applicable to numerical data.
For full population data, standard deviation dot e is utilized, calculating variance across the entire population.
To calculate standard deviation for sample data, select the range, close the bracket, and press enter to obtain the value.
The large function returns the nth largest value from a sample, with the option to specify the position of the value.
Changing values in a dataset affects the ranking of largest and smallest values when using the large function.
The small function identifies the nth smallest value in a dataset, with the ability to adjust the position of the value.
Altering values impacts the ranking of smallest values in a dataset when using the small function.
The correlation function in Excel determines the correlation coefficient between two variables, indicating the strength and direction of their relationship.
A correlation coefficient close to 1 signifies a strong positive relationship, while -1 indicates a strong negative relationship, and 0 implies no relationship.
Using the correlation function with arrays of data helps analyze relationships, such as age and glucose levels or stock price changes.

01:06:20

Enhancing Column Charts for Data Comparison

Column charts use vertical bars for data comparison, often for comparing information like in the case of 2016 and 2017 sales data.
Mumbai's sales data in the column chart lacks visibility without a table, requiring a mouse-over to reveal the exact numbers.
To add data labels in the column chart, select each bar individually, right-click, and choose "add data label" to display the numbers.
Data labels can be added more efficiently by selecting the chart, clicking the plus sign, and choosing "data labels" to automatically add them to all bars.
The Y-axis in the chart displays numbers, aiding in data interpretation when data labels are absent.
To remove the Y-axis, go to design mode, select the chart, access "add chart element," choose "axes," and deselect the primary vertical axis.
Grid lines in the chart can be deleted for a cleaner look by selecting them and pressing delete or using the plus sign and deselecting grid lines.
Adding a chart title is essential for clarity, achieved by selecting the chart, adding a title like "Sales Comparison," and specifying units like "millions."
Line charts are ideal for displaying trends over time, like temperature changes over days, showcasing a clear progression or regression.
Pie charts visually represent proportions of values, with each slice representing a percentage of the whole, aiding in easy comparison of different categories' contributions.

01:20:30

Excel Charts and Pivot Tables Simplified

The text discusses the impact of finance, marketing, and effort on the environment.
It mentions the values added by these departments and the removal of grid lines for a better look.
The data is represented in a range from 0 to 200, 200 to 400, and 400 to 600, each denoting different aspects.
It explains how to work with charts in Excel and make necessary changes to identify the best chart.
The text then transitions to explaining pivot tables and their usefulness in daily tasks.
It highlights the simplicity and speed of creating pivot tables with well-organized source data.
Detailed steps are provided on creating a pivot table in Excel, selecting ranges, and choosing new or existing sheets.
The text elaborates on the pivot table fields, including values, row area, column area, and filter area.
Instructions on sorting data in a pivot table and using slicers for filtering are detailed.
The process of creating a chart from a pivot table, customizing it, and switching between row and column data is explained.

01:34:55

Excel Analysis Tool Pack for Data Analysis

Branch option allows selection and analysis of data, needing to link Pivot Table 2 to Pivot Table 1 for synchronized data display.
Slicer linking process involves right-clicking on the slicer, selecting "report connections," choosing both Pivot Tables from Sheet 1, and clicking OK.
Linked slicer enables automatic data display for selected branches in both Pivot Tables simultaneously.
Multiple branch selection through the slicer results in data display for all selected branches across both Pivot Tables.
Excel's Analysis Tool Pack provides tools for financial, statistical, and engineering data analysis.
To load the Analysis Tool Pack, go to Excel's file tab, select options, choose the Analysis Tool Pack, and click OK.
Regression analysis in Excel involves selecting Y and X ranges, checking labels, specifying the output range, and checking residuals before clicking OK.
Regression analysis output includes basic statistics, ANOVA information, regression line details, and scatter chart visualization.
Waterfall development model involved shipping monolithic applications, facing challenges with changing requirements and lengthy deployment times.
Agile development philosophy emphasizes rapid deployment of organized code chunks, iterative development, constant feedback, and smaller, manageable services.

01:50:18

Agile and Scrum in E-commerce Development

Front end is now separate from catalog, which is called upon when a product is selected for purchase.
Clicking "buy now" leads to a shopping cart, followed by email and text notifications post-payment.
Services like front end, catalog, and shopping cart are distinct but work together in synergy.
Microservices ensure separate development, preventing one service from affecting another.
Agile values people over processes, working software over documentation, and customer collaboration over rigid contracts.
Agile allows for responding to change rather than strictly following a plan.
Agile principles include satisfying customers, welcoming changing requirements, and delivering working software frequently.
Agile emphasizes constant feedback, maintaining a steady pace, and sustaining technical excellence.
Scrum is an iterative philosophy involving planning, building, testing, and reviewing in cycles.
Scrum involves roles like product owner, scrum master, and development team, with tasks broken down into product backlogs and user stories for prioritization and execution in sprints.

02:06:25

Agile Scrum: Rapid, Iterative Software Development Framework

Scrum involves breaking down the application development process into shippable parts every two weeks, based on set priorities by the product owner and scrum master.
The methodical aspect of Scrum focuses on breaking tasks into smaller, manageable chunks for clear understanding and execution.
Rapid deployment in Scrum allows for immediate testing of code in the development environment, ensuring instant feedback for necessary adjustments.
Extreme Programming, an earlier framework, emphasized people-centric practices, discipline, rapid deployment, and customer input, serving as a precursor to Scrum.
Lean programming principles aim to eliminate waste, amplify learning, empower teams, and ensure cross-functionality for efficient development.
Kanban, similar to Scrum, involves continuous task management without fixed sprints, utilizing queues like build, test, and ship for seamless progression.
Agility in software development, exemplified by Netflix's daily deployment of over 1,000 changes, showcases the benefits of rapid feedback and organized team practices.
Crystal, another framework, focuses on philosophical, technical, and software development aspects to enhance team collaboration and product delivery.
Scrum contrasts with the waterfall model by breaking projects into smaller, iterative cycles, allowing for continuous planning, building, testing, and reviewing for quicker product delivery.
Scrum, a lightweight and simple framework, promotes effective team collaboration through agile practices, emphasizing incremental releases and maximum value delivery.

02:23:08

"Scrum Master: Agile Leadership for Team Growth"

Scrum Master or Agile Approach involves organizing people efficiently, exemplified by queuing by height with minimal time consumption.
Two solutions to the problem: Supervisor Approach (traditional, time-consuming) and Agile Approach (Scrum Master allows self-organization).
Agile Approach in software development involves evolving requirements through collaborative, self-organizing teams under the guidance of a Scrum Master.
Scrum Master ensures self-organization, continuous iteration, and testing, responding to problem unpredictability.
Scrum Master is distinct from a supervisor, focusing on team self-organization and growth, not micromanagement.
Scrum Master is not a secretary but a leader who coaches, recruits, and fosters team cohesion and growth.
Scrum emphasizes empiricism, promoting fact-based, experience-based, and evidence-based work for business and organizational agility.
Empiricism pillars include transparency, inspection, and adaptation, fostering trust, collaboration, and continuous improvement.
Scrum life cycle involves product owner, backlog, Sprint planning, development, review, and retrospective for iterative progress.
Sprint is a time-boxed iteration for planning, building, testing, and reviewing, ensuring predictability and adaptability in product development.

02:42:21

"Mastering Agile Scrum and Business Analysis"

Daily scrum is held at the same time and place each day, focusing on three questions: what was done yesterday, what will be done today, and what impediments exist.
Scrum Master must be aware of challenges faced by the team and ensures the daily scrum is conducted within a 15-minute time box.
Sprint review inspects the increment and adapts the product backlog if necessary, involving collaboration between the scrum team and stakeholders.
Sprint review includes the product owner explaining completed backlog items, the development team demonstrating their work, and collaboration on future steps.
Sprint review is time-boxed at one hour a week for shorter Sprints and four hours for one month's Sprints.
Sprint review aims to provide valuable input for subsequent Sprint planning based on changes in the marketplace, product capabilities, and competition.
Sprint retrospective is a three-hour meeting for one month's Sprints, focusing on inspecting the team's performance and planning improvements for the next Sprint.
During the Sprint retrospective, team members answer three questions: what went well, what didn't work well, and what should be done differently.
The Sprint retrospective aims to identify improvements to increase product quality and effectiveness for the next Sprint.
Becoming a business analyst requires skills in Power BI, Tableau, data analysis, SQL, Python, Excel, and project management, as highlighted in job descriptions from companies like Disney, Dell, and Puma.

03:00:43

Essential BI Tools and Skills for Analysts

Companies use various BI tools like SAP Business Objects, MicroStrategy, SAS BI Intelligence, Qlik Sense, Zoho Analytics, and Power BI.
Some tools are free or offer trial versions, allowing users to choose based on their needs.
BI analysts must understand the data landscape, databases, and applications thoroughly.
Responsibilities include data collection, review, validation, gathering end-user requirements, and maintaining customer data.
Educational specialization, expertise, career experience, and interest in business intelligence are crucial for BI analysts.
Agile software development involves iterative development, adaptive planning, and collaboration for better outcomes.
SDLC is a process used in software development to design, develop, and test high-quality software.
Technical questions may include defining business intelligence, its uses, data flow, control flow, OLAP, and ETL.
SQL Server tools like SSIS, SSAS, and SSRS are essential for integrating, analyzing, and reporting data.
SQL queries, differences between WHERE and HAVING clauses, and views vs. materialized views are common topics in interviews.

03:23:36

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Promotes checking out more videos in the playlist
Urges viewers to subscribe to the Edureka channel for further learning opportunities

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