Data Analytics

A Comprehensive Guide To The Data Analytics Course by KPMG

In this digital era, the horizon of a data analytics career has grown immensely. We consume a lot of information on various platforms every day. This information is a group of collective data that has an analytical and logical meaning. To understand and formulate the information we need to do data analysis. For executing data analysis, we need certain raw data that have no particular meaning and an expert who can convert this raw data into certain information or output for a particular target segment using various tools and methodologies. To meet this expanding need, online data analytics courses are offered by various institutes. In this article, we will highlight the business analytics and data analytics course by KPMG which is one of the best learning academies that has designed a professional training program in the data science and data analytics domain.   

 

What is Data Analytics?

Data analytics is the science of assessing and analyzing raw data to find certain answers or trends. It helps to identify patterns in raw data and extract and conclude valuable information from them. In other words, data analytics is the process of analyzing and exploring large amounts of data to find unseen patterns and hidden trends, locate correlations, and extract valuable insight to make certain business predictions and help make certain decisions.

Data analytics is an extensive term that includes many various types of data, such as big data, metadata, real-time data, machine data in the form of log files, transactional data, web server data, customer-related data, and social media data.

Any such kind of information can be put through data analytics techniques to get details that can be used for improving efficiency. Techniques reveal metrics that would otherwise be lost in the pool of information. This information helps optimize the process and improves the productivity of the business.

 

Why is Data Analytics Important?

It is important for the following reasons: –

For informed decision-making, organizations can make decisions faster with an effective data strategy that ensures the data is usable for business purposes. For instance, an online jewelry store owner can help them decide which design is returned, which design to include, which designs are sold more, which designs are sold less, etc.

2. Understand the target audience: When your assumptions are backed by data, you know what demographics define your target segment, what motivates the target audience, etc.

3. Identify and solve the problem. Let’s assume that an electronics manufacturer noticed that a particular model of TV was returning a lot. When they audited, they came to know that the particular model had certain defeats. Since they identified the problem, they can now solve that issue and decrease the return units of TV.

4. Adjust budgets: Data analytics can highlight areas in the business that need improvement or that are performing well. For example, if a company feels that X product or solution is growing more than Y, they can allocate more budget or resources for the X product. Thus, it helps the company generate enough revenue through proper data analysis.

 

The Process of Data Analytics

Since we know that data analytics help an organization make decisions, It is more than the data analysis. In data analysis, the whole component is broken into small parts or data, while in data analytics, it is the science of logical analysis. Let’s see the steps or processes involved in data analytics.

 

1. Understand the goal and/or problem: Every organization has its own goal to achieve, but at some point, they encounter the problem too, so planning a rewarding solution is the basic step in the analytics process.

 

2. Data Collection: To make a decision for a certain scenario, data needs to be collected. Data collection is the process of gathering raw data from all the relevant sources in order to find a solution to a problem or to achieve the desired goal. Data can be collected in two ways: the primary data collection method and the secondary data collection method. Almost all-time past data plays a critical role in future decisions. Appropriate and correct data collection is necessary to ensure quality assurance, make informed business decisions, and maintain research integrity.

 

3. Data Cleaning: Data cleaning is the process of removing or fixing incorrect, corrupted, duplicate, incomplete, or incorrectly formatted data within a dataset. In the above step, data is collected from various sources. All the data collected in the above step may not be relevant or correct. Data collected will often be disorderly, of unwanted value, rogue data, etc. If the same data without cleaning is used, then the analytics that are predicted based on it will be flawed, resulting in incorrect steps or incorrect decisions.

It is like creating a strong fountain in a building. If the foundation is right, then the building will be strong, and if the foundation is wrong, the building will collapse. Data cleaning has other benefits too, such as avoiding mistakes, improving productivity, staying organized, improving mapping, and avoiding unnecessary costs. Data cleaning can be done by programming languages, visualization, and proprietary software.

 

4. Data Exploration and Analysis: After gathering and cleaning the data, the next vital step is to do data exploration. The process of knowing your data in depth is called data exploration. Data exploration refers to the step in which data analysts use statistical techniques and data visualization to describe datasets such as accuracy, quantity, and size to understand the initial pattern and dataset characteristics. Data exploration can be done with the use of languages such as R and Python. We can use data mining techniques, data visualization, business intelligence tools, etc. to forecast the future results of the data.

Eg: Can you predict sales and profits for the next quarter?

Can minimize order cancellation rate

 

5. Interpret the results: The final step is to interpret the results and validate the outcomes. It helps to gain insights that can support appropriate data-driven decision-making.

 

Tools for Data Analytics

Since we have seen the steps involved in data analytics, let’s go through some tools and programming languages that can help perform analytics better.

R is an open-source programming language that is best suited for numerical and statistical analysis. It provides a large ecosystem of libraries for data analysis and visualization. R also has tasks like classification and regression, and it also has many packages and features for developing an artificial neural network. You can also wrangle data. It can be put into three main groups: manipulation of data, analysis of a number, and visualization of data.

 

Python is a high-level, object-oriented open-source programming language known for having a syntax that is easy to understand. It can be used in data science, data analytics, automation, scripting, and web applications. It has a range of libraries for data modeling, data manipulation, and data visualization.

 

Power BI is a business intelligence tool that turns raw data into visual and interactive insights. It has drag-and-drop functionality. It supports multiple data sources along with various features that are visually appealing for the data.

 

Tableau is a powerful, fastest-growing, and leading analytics and data visualization tool. It helps simplify raw data in a very easily understandable and interactive format. A person who does not have any coding knowledge or technical expertise can also create customized dashboards. It can use a range of data, data of all types and sizes, and translate it into visually appealing worksheets and dashboards.

 

SAS is a software developed by the SAS Institute that performs various analyses. SAS stands for Statistical Analysis System. It is useful for performing business intelligence and data management; it conducts predictive analytics, multivariate analyses, and advanced analytics.

 

Types of Data Analytics

1. Text analysis (what is happening) Text analytics is the process of extracting meaningful information from unstructured text. It is also like data mining, which includes the capture of data from a large amount of unstructured text-based data sources such as reviews, support incidents, emails, social media, and so on.

Below are the few methods used to implement text analytics:

Word frequency: It specifies the most frequently used word in that data. For example, a clothing store owner monitors social media tags and mentions and calculates the frequency of the negative and positive words used by the customers, like “worth “,  “fabulous,” “cheap, etc., to determine how the customer had an experience after purchasing that particular garment.

Keyword Extraction: It identifies the most relevant terms. For example, instead of screening all the reviews, a brand will use a keyword extractor to summarize the words and phrases that are most relevant.

Language detection: It indicates the language that you have used for the text. For example, in customer care support, a brand may use language detection to identify the language of the ticket raised and connect customers with the correct representative.

 

2. Statistical Analytics (What Happened) – It pulls the raw data from the past to identify the necessary trends. In these two categories, one is descriptive and the other is inferential. Let’s see each of them.

Descriptive: It looks at numeric data and calculations to decide what actually happened. For example, how much sales did a brand do? How many views does a brand have in a week or month? What is the customer satisfaction rate? How are the campaigns performing or performing? There are a few methods through which we perform descriptive analysis.

a) Measure of central tendency: It uses mean, median, and mode to evaluate results. For example, a game launched by an accompanying person may use this information to determine which age group is accessing the game.

b) Measure of dispersion: It measures how data is distributed across the range. For example, HR may use this method to decide the salary for that particular profile or post.

c) Measure of frequency: It helps to identify how frequently an event has occurred. For example, a restaurant sends a survey email asking customers about what their favorite dessert is and uses this particular method to decide which dessert is selected and how many times.

Inferential: this method is mainly used when the population you are interested in analyzing is very large. There are a few methods through which we perform inferential analysis.

a) Regression analytics: It shows the effect of independent variables on a dependable variable. For example, a cosmetic brand uses this method to determine the relationship between late delivery and a bad review.

b) Confidence interval: It indicates how accurate an estimate is. For example, a brand wants to determine about a new product how confident they are that the person surveyed can make up their target segment.

c) Hypothesis testing: It identifies which variable impacted a particular topic. For example, if the sale of a particular product increased because of a certain marketing campaign.

 

3. Diagnostic analytics (why it happened) – It is also called root cause analysis (RCA). It detects the cause of a particular result or event. There are a few methods to perform this analysis. Let’s see one by one.

Correlation analysis determines the relationship between two variables. For example, the rainy season will increase the sale of raincoats and umbrellas.

Data drilling uses business intelligence to reveal a detailed view of the data. For example, revenue generated by a firm in particular cities for particular months. Time-series analysis analyses the data collected over a period of time. For example, there is a sale of X product between April and October every year.

 

4. Predictive analytics (what can happen) This technique uses past or historical data to predict or foretell future trends or outcomes so that companies can make future strategic decisions. There are a few methods through which one can perform this analysis. Decision tree: It is a flowchart reflecting a clear path to a decision.

Machine learning uses artificial intelligence (AI) to predict outcomes. For example, a search engine uses AI to recommend certain products based on browsing history.

 

5. Prescriptive analytics (what should be done) – The highest level of analysis aims to find the best action plan. By using the insights of data, data-driven decisions can be made. It helps the companies make relevant decisions when uncertainty is faced.

 

Thus, using various types in combination provides a well-versed understanding of a company’s opportunities and needs.

 

Data Analytics Applications 

Data analytics is used in almost every business sector; let’s explore a few of them:

1. Retail: various data analytics techniques help retailers understand their customer’s buying habits, predict trends, and recommend new products based on their browsing history.

They optimize the supply chain and retail operations at every step of the customer journey.

2. Manufacturing: By using data analytics, manufacturing sectors can solve supply chain issues, discover new cost-saving opportunities, and overcome labor constraints.

3. Banking sector: Banking and financial institutions use analytics methods to find out loan defaulters and customer churn-out rates. It also helps in identifying fraudulent transactions immediately.

4. Logistics: Logistics companies use data analytics methods to develop new business models and optimize routes so that delivery arrives on time and in a cost-efficient manner.

 

Data Analytics Course by KPMG

The Data Analytics course by KPMG in India is a comprehensive course for both students and working professionals. They offer in-depth experimental and instruction-led training in data science, data analytics, and machine learning. The course is designed to help an individual understand the concepts of visualization, data analytics, and machine learning using tools and languages like advanced Excel, R, and Tableau.

The data analytics course by KPMG covers many factors of data science, data analytics, and machine learning, which include case studies and the implementation of all the learned principles, tools, and techniques in real-world scenarios. The training and study material of the data analytics course by KPMG satisfies the assured demand for competent data analytics employees.

After completing the course, an individual will be able to analyze the raw data, predict certain trends, and create dynamic, visually appealing data with the help of various tools and techniques of data analytics.

 

Unique Advantages of the Data Analytics Course by  KPMG

Focuses on critical and crucial elements of business problems.

It is not only created for programmer individuals but also non-programmer individuals can learn.

The course is designed, developed, and delivered by a team of data professionals from KPMG.

The data analytics course by KPMG focuses on a completely practical approach to learning.

Case studies of an industry to provide a comprehensive overview.

The course is designed in such a way that students can understand the depth of business cases in an organization.

LMS access for 90 days after course completion.

A five-week, instructor-led classroom training designed to train professionals and students from various backgrounds with the fundamentals of data analytics

 

Check here the other best data analytics courses:

 

The Curriculum Covered in the Data Analytics Course by KPMG

Basic fundamental concepts of data and business analytics

Spreadsheet modeling using Advanced Excel

Data cleaning and data analysis using R

Data wrangling using R

A predictive model using R

Storytelling using Visualization

Techniques of different statics for business decision-making

 

What will an individual learn after completing the data analytics course by KPMG?

After completing data analytics course by KPMG, you will learn:

Create dashboards

Create stories

Build real-time models using different tools and techniques.

Link live data of a website to Excel

We will be able to learn about the target segment of customers by learning their patterns.

Use R to clean data and analyze it

Use statistics to make better business decisions and future predictions.

Non-experienced individuals can also kick-start their career in data analytics.

 

Evaluation of Data Analytics course by KPMG

All individuals have to submit assignments. At the end of the training, there is a multiple-choice theory paper and a piratical exam. Scoring 65% in both exams is mandatory for the participants. Participants who fulfill the criteria will be awarded a certificate for successful completion of the course by KPMG.

 

For more details, contact us below.

Email: [email protected]

Contact : 91 94474 94118 (Lijin) and +91 9003381790 (Kaushik)

 

FAQs

 

1.      What is the career growth in data analytics?

Every year, there will be a 15% demand for data analyst jobs between 2020 and 2030. The salary that an individual will get depends on the industry and the function of that industry. Since every company wants to grow exponentially, they will prefer the implementation of data analytics so that they can make better decisions. Hence, job roles related to data analytics will also be in demand.

 

2. Is pursuing a data analytics course by KPMG beneficial?

It is one of the best courses in data science, data analytics, or business analytics offered by KPMG since it covers the in-depth fundamentals of data analytics and machine learning, tools, and techniques that can be used in various real-time business scenarios. They also make students learn case studies and provide a platform to practically implement all the modules learned.

 

3. What positions can one get after completing the data analytics course by KPMG?

Data Engineer

Data Analyst

Data Scientist

Business Analyst

Operation Analyst

Marketing Analyst

 

4. Which industry uses data analytics?

Since data analytics is to collect, clean, and analyze data, The outcome of the data can be helpful in predicting future trends, solving particular business problems, understanding patterns, reviewing sales, etc. It can be used in many industries. A few are listed below: –

Retail and wholesale

Energy and Utilities

Government

Logistics and supply chain

Insurance

Manufacturing

Education

Pharma and healthcare

Banking and securities

Media & Entertainment

 

To Sum Up On Data Analytics Course by KPMG

To conclude the data analytics course by KPMG will help an individual to establish themselves in the data science domain even if they don’t have zero experience. They teach tools, techniques, and programming language of data science or data analytics, artificial intelligence, and machine learning including hands-on experience. They provide exposure to various case studies across the domain and industries so that an individual consumes knowledge of minute aspects. After course completion an individual will be able to predict forecasts depending on the raw data and also, they will be able to present visually appealing data. The data analytics course by KPMG is worth pursuing for those who want to opt data science domain as a career.

 

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