Data Analytics Course By KPMG With Practical Training
Unleash your data analytics potential. Develop profession-ready abilities for a data analyst career that is in high demand. No education or work history is necessary. Data-related courses have emerged online as a result of the continuously expanding need for data analysts. However, a lot of course offerings fall short of giving a comprehensive learning experience that covers the complete analytics pipeline. In this article, I’ll talk about the Data Analytics Course by KPMG as one of the top online learning resources for finding the greatest data analytics courses. Since an analyst’s profession requires a variety of talents, I’ve included the top courses in each category. Hopefully, this classification will enable you to better understand the Data Analytics Course by KPMG.
You may find information on what data analysts accomplish, the skills they require, and suggestions for how to get started as data analysts if you’re still having problems choosing a course.
Data Analytics: What is it?
Data analytics refers to a collection of quantitative and qualitative methods used to extract knowledge from vast amounts of data. Data analytics is regarded as the engine driving the digital economy since it enables businesses to create consumer profiles and provide their clientele with specialized goods and services. Organizations may now manage corporate data in an efficient manner thanks to the development of big data apps. The following tools are included in the big data analytics domain:
- R programming
In a world where gathering data and statistics are becoming more and more important, data analytics helps individuals and organizations ensure the authenticity of their data. Using a variety of tools and approaches, a set of raw information may be transformed into informative, educational insights that direct decision-making and careful management.
What Categories Do Data Analytics Have?
1. Descriptive Analytics
A type of statistical analysis that turns raw data into a format that is easy to understand by using data mining and data aggregation. It enables users to learn from past actions and forecast how they may affect results in the future. Introduction and retrospective in character, descriptive analytics may be used to:
Draw statistical information, such as the overall amount of an inventory, the cost per client, and the variation in sales
Reveals information on the operations, finances, and output of the firm
Summarises and discusses several business topics
Lowers website traffic and boosts social media interaction.
2. Predictive Analytics
Data gathering, machine learning, and machine simulation are used in this type of advanced statistics for fraud prevention and detection and predictive data administration. The main foundation of predictive analytics is a probability, which may be used to assess data that is currently lacking or has not yet been collected. To find data trends, it merges past data from systems like ERP, CRM, HR, and POS.
Businesses Use This Type of Analytics Because It:
- Depending on the data provided, offers businesses actionable data
- Calculates the probability of a future event
- Use statistical models and methods to analyze the relationship between distinct datasets.
- Analyses purchase habits and sales activity for trends
- Estimates the needs for supply chain, operational, and inventory inputs
- Determines the likelihood that consumers will make future credit payments on time by contributing to the production of credit ratings
- Accurately forecasts the organization’s strategy for the future
3. Diagnostic Analytics
Diagnostic analytics uses data mining techniques to provide retrospective answers to business questions. This generally entails working on a dashboard and aids in identifying the reason for an unexpected result. Examining the quantity of it is crucial in appraising social media initiatives.
- Mentions for a post
- Page views
4. Prescriptive Analytics
Before making decisions, prescriptive analytics provide guidance on potential consequences. It makes use of a variety of instruments and methods that may be used with datasets like:
- Facts from transactions and the past
- Current data feeds
- Massive data
The Following Technologies and Strategies Make Up Prescriptive Analytics:
- Business regulations
- Automated learning
- Techniques for computational modeling.
It is a very complicated type of data analysis, and data scientists who are familiar with prescriptive models typically perform it.
Prescriptive analytics is frequently used by larger firms to:
- Obtain direction and assistance on the day-to-day operations of the business.
- Optimize the supply chain’s production, planning, and inventory.
- Deliver the appropriate goods at the appropriate time and improve customer experience.
- Establish for the company a clear analytical route based on guidelines and suggestions.
- Many quality assurance programs, including the widely used Six Sigma methodology, are supported by data analytics in the finance industry. It is very difficult to optimize anything if you aren’t accurately monitoring it, whether it be your weight or the million-unit failure rate in a production line.
- The travel and hotel industries, whose turnaround times are sometimes short, are among the industries that have embraced the utilization of data analytics. This industry may compile customer data and pinpoint issues as well as their root causes.
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Techniques for Data Analytics
The processing of data and the extraction of information may be done using a variety of analytical techniques and procedures. Several of the most popular methods are listed here.
- Regression Analysis
Entails looking at how the multiple regressions are connected to determine how changing one can affect changing the other.
- Factor Analysis
Involves reducing a huge data set to a smaller data set. It is intended that by utilizing this method, traits may be found that might be more challenging to notice in the past.
- Cohort Analysis
Is the division of a data collection into sets of related data, frequently divided into a consumer demographic. This makes it possible for consumers of data analytics, such as data analysts, to delve further into the statistics relating to a particular subset of data.
- Monte Carlo simulations
Simulate the likelihood of various scenarios. These simulations, which frequently include many values and variables and frequently have better-predicting skills than other data analytics techniques, are frequently utilized for risk reduction and loss prevention.
- Analyses of time series
Collects data through time and establishes a link between the importance of a data piece and its recurrence. This technique for data analysis is widely used to spot cyclical trends or forecast financial results.
The Importance of Data Analytics
Data analytics is significant since it facilitates the performance optimization of enterprises. Businesses that find more efficient ways to operate might help save expenses by implementing such practices into their business strategies. Data analytics may also be used by businesses to analyze customer preferences and trends, enhance corporate decisions, and create new, better products and services.
Workflow and efficiency may be improved in a variety of businesses by implementing data analytics. Applications for data analytics are numerous and can provide organizations with an advantage in a cutthroat market.
One of the first industries to use data analytics in business and finance is the financial sector. Additionally, it aids in risk assessment and market trend prediction. Data analytics, for instance, is used to calculate a person’s credit score since it considers a variety of indicators to assess the risks associated with lending money. Data analytics is also utilized to stop fraud.
Data analytics goes beyond only emphasizing higher ROI and profitability. In addition, it may be applied to prevent crime in the healthcare sector. It makes superior data-driven decisions for organizations by generating insightful insights based on the data using modern analytical methods and statistics.
Data analytics may be used for a virtually limitless number of purposes, and it is crucial to make processes effective, productive, error-free, and seamless. Data analytics prospects are expanding as a result of the startling rate at which data is being produced.
Data Analytics Process Steps
More than only data analysis is involved in data analytics. The majority of the necessary effort is done in advance while creating datasets. The actions listed below are necessary to undertake data analysis:
- Data Collection
Data collection must rigorously correspond to the business objectives because it is the cornerstone of all other operations. Data can be gathered via Big Data technologies, database queries, or web scraping.
Even though there is a vast amount of data available today, only relevant data can offer the correct insights to meet corporate objectives. Present-day situations include data in both structured and unstructured formats, including text, photos, and videos, which are later divided for additional data analysis.
- Data Processing
The majority of the time, acquired data is unstructured and has few missing values, making analysis challenging. By purifying and changing data into the necessary forms, data processing ensures data quality. Imputation techniques are used in this procedure to deal with null values and outliers, and various transformation techniques are used to spread the data evenly.
- Data Analysis
To comprehend data and draw conclusions, data analysis is done utilizing a variety of applications and tools. To develop statistical data models, such as correlation and regression analysis to look at correlations, data analysis requires a set of fundamental abilities. In this technique, linear, continuous qualities that are blocked inside features may be identified using data visualization tools.
- Data Interpretation
The process of evaluating data using established criteria in order to draw a well-informed judgment is known as data interpretation. By developing interactive Dashboards and Reports, Data Interpretation assists Data Analysts in helping the audience grasp the importance of numerical data. This procedure unearths trends and statistics to obtain important information.
The Advantages of Data Analytics
The Following is a List of Some Advantages of Using Data Analytics:
- Cooperate and Streamline
Data analytics is utilized to simplify complicated issues that have previously been difficult to solve effectively. Users are able to scale up the application of Data Science approaches thanks to platforms like Hadoop and Spark. The efficiency and productivity of the entire company are increased when data analysis, area of business, and IT teams collaborate on big data analytics initiatives.
- Take Action by Identifying Anomalies
Data is being produced at an astounding rate, and detailed analysis of a large amount of it enables you to spot abnormalities and respond to situations as they arise. Users may summarise, filter, and acquire insights from data with the use of data analytics, and they can also address anomalies before they become major issues.
- Conceptualize, Check, Manage, and Have Faith
With the aid of data analytics, users may automatically install new sophisticated algorithms at the edge and retrain existing ones in order to produce more accurate and reliable results.
- React Quickly and Accurately
It enables companies to react to events in a split second, ensuring that the appropriate action is performed at the appropriate moment. Utilize serverless contemporary apps with a small footprint that are built on open-source technologies and microservices for edge scoring.
Data Analytics Applications
As a result of digitalization, every sector of the market is now data-driven.
Among the Uses for Data Analytics Are:
- Marketing and Sales
By examining behavior and preferences, data analysis enables firms in comprehending clients from the first moment of contact till the point of purchase. Furthermore, data analysis has an impact on marketing activities by giving stakeholders the key metrics they need to create effective digital marketing strategies.
The field of data analysis is making substantial breakthroughs in the search for illness cures and drug side effects. The healthcare sector is thriving thanks to tools that prescribe prescriptions, chatbots that assess health, and personalized health advice.
What Advantages Do Organizations Get from Data Analytics?
- Programming languages used in data analytics include those that aid in data analysis and address business-related issues.
- Assists in determining the strategies necessary for data gathering, processing, and insight extraction.
- Ensures that insights are applied carefully based on the sort of business, the industry it is in, and the specific requirements.
- Assist companies in problem prediction and solution mapping before problems arise.
- Helps the firm incorporate better resources and viewpoints, which promotes the organization’s growth.
Which Positions are Available for Data Analysts?
No one is surprised that data analysts need to be skilled in data analytics. However, as soon as you start working with data analytics, you become qualified for a wide range of occupations associated with data analysis.
Here is a Sample of Them:
- Business Intelligence Analyst
- Data Analyst
- Data Scientist
- Data Engineer
- Quantitative Analyst
- Data Analytics Consultant
- Operations Analyst
- Marketing Analyst
- Project Manager
- IT Systems Analyst
Reasons Why You Should Become Data Analyst
Most individuals consider job security to be extremely important, and a career in data is considered to be reliable because of the great demand for these professions. According to much research, the job of a data analyst will be the one that is most in demand in the foreseeable future. A major benefit is knowing that your work is most likely to be stable (or, at the absolute least, that it won’t be difficult to find a successor).
It may be a thrilling experience to be employed as a data analyst since you have the ability to work on various projects across a variety of sectors. Another important consideration for so many people when selecting a job route is salary. There is also no denying that working as a data analyst will pay you financially. According to, the typical starting salary for a data analyst in the US is $92,038 per year.
Data Analytics Course by KPMG
The Data Analytics Course by KPMG India provides both students and professionals with instructor-led and practical training in data analytics, machine learning, and in-depth learning. With the use of technologies like R, Python, Tableau, and programming languages, the course instructors can assist you in comprehending the principles of data analytics and machine learning.
The Data Analytics Course by KPMG goes in-depth, covering many different facets of data analytics and machine learning, including practical modules, case studies, and the implementation of these principles in real-world business situations. In order to promote thorough experience with statistical and analytical methods and techniques, participants are presented with simulated industrial data.
The breadth of data analytics as a job has greatly expanded in this digital world. Development of the most sophisticated training for Data Analytics Course by KPMG India is to satisfy the inescapable need for qualified and competent data analytics employees.
With the use of programs like Python, R, and Tableau, this training seeks to provide students with hands-on experience with data science principles. Participants will be able to generate dynamic visualizations and graphs, design prediction models, process and analyze data, and study practical implementation in an industrial setting thanks to the ideas presented in this program.
The Competitive Advantage of the Data Analytics Course by KPMG
- Concentrates on the strategic elements of business issues
- Created with both programmers and non-programmers in mind, with the goal of directing both groups toward data analytics
- Designed and delivered by a group of data experts from KPMG in India
- A strategy that has been thoughtfully designed to provide students with a complete grasp of business cases in the context of organizations
- Case studies include all organizational functions to provide a complete picture.
- A method that is entirely practical.
Curriculum Details of Data Analytics Course by KPMG
- Packages and syntax for data science
- Business statistics for decision making
- Data wrangling using R
- Visualization using dashboard and stories in Tableau
- Predictive modeling using Python
- Web scrapping using Python
- Text and sentiment analysis using Python
- Automation of R and Python scripts
- R and Python may be used to clean and analyze data.
- When making business choices, use descriptive and inferential statistics.
- Make tales and dashboards
- Create real-time prediction models using the right methods.
- Get information from websites and analyze it
- Assess client mood by examining comments and reviews
- Automate routine analytics work
Who Might Participate:
- Engineering professionals
- Business grads
- Individuals looking to launch a career in data analytics who have no more than five years of professional experience.
A 5-week classroom training program offered by instructors that aim to teach the principles of analytics to professionals from a variety of backgrounds
Email Id: [email protected]
Contact: +91 94474 94118, +91 73495 06076
Frequently Asked Questions
Q1. Is the Data Analytics Course by KPMG worthwhile?
One of the best data analytics degree programs for aspirant data analysts is the Data Analytics Course by KPMG. The Data Analytics Course by KPMG goes in-depth, covering many different facets of data analytics and machine learning, including practical modules, case studies, and the implementation of these principles in real-world business situations.
Q2. What does the Data Analytics Course by KPMG entail?
Analytics has the potential to produce a lot of value, according to the network of data and analytics experts at KPMG. By releasing value for our client’s businesses, we can help them expand, gain genuine, useful insights, manage risk, and save costs.
Q3. When and where are data analytics used?
Businesses also utilize data analytics to help them make decisions. Data scientists and analysts use it to do research. Companies may build goods, analyze their advertising efforts, customize the content, and establish content strategies with the use of data analysis.
Q4. What makes data analytics useful to study?
A business may utilize data analytics technologies to evaluate raw data and identify trends in order to derive useful information. As a consequence, data analysis helps firms in a variety of ways, involving decision-making, developing marketing strategies, enhancing customer experiences, and optimizing operational processes.
Conclusion on data analytics course by KPMG
The Data Analytics Course by KPMG encompasses a broad range of topics related to analytics and artificial intelligence, including hands-on courses, legitimate business applications, and research papers. Participants are given simulated industry data to encourage in-depth exposure to statistical and analytical tools and procedures. This Data Analytics Course by KPMG has been created to develop profession-ready abilities for a data analyst career that is the most sought-after profession by organizations and businesses.