Top Data Analytics Courses At XLRI With Live Assignments
Assessing data sets to identify trends and make conclusions regarding the information they contain is the process of doing data analytics. Data analytics is increasingly carried out with the use of specialized software and system. To help businesses, make better business decisions, data analytics technologies and methodologies are widely used in the commercial industry. Analytics tools are also used by scientists and researchers to support or refute scientific models, theses, and postulates. The below article will guide you on the list of Data Analytics Courses at XLRI for better career growth.
Businesses may boost customer service efforts, optimize marketing campaigns, and increase revenue with the aid of data analytics initiatives. Additionally, the analytics give companies a competitive edge over rival companies by allowing them to react swiftly to new market trends. But enhancing business performance is data analytics’ core objective. The data that is analyzed might be made up of either new information that has been analyzed for real-time analytics or historical records, depending on the specific application. It may also come from a combination of internal systems and outside data sources.
Inside the Data Analytics Method
Applications for data analytics go beyond just data analysis, especially for advanced analytics projects. A large portion of the necessary work is done in advance during data integration, preparation, and collection. Analytical models are then tested, revised, and developed to make sure they produce accurate results. Data engineers, who are involved in the process of creating data pipelines and aid in the preparation of data sets for analysis, are frequently included in analytics teams along with other data analysts and data scientists.
Data collection is the first step in the analytics methods. Data scientists decide what information they require for specific analytics use, and then they either put it together alone or with the help of data engineers and IT employees. It may be necessary to combine data from many source systems using data integration routines, turn it into a common format, and then load it into an analytics system, such as a Hadoop cluster, data warehouse, or NoSQL database. In other situations, the collection procedure can entail selecting a pertinent subgroup from a stream of data flowing into, say, Hadoop. The data is then transferred to a different system partition so it may be examined without compromising the entire set of data.
Finding and fixing data quality issues that could compromise the accuracy of analytics uses is the next step once the necessary data has been collected. To make sure a data set contains consistent information, and that duplicate entries and errors are removed, this includes executing data cleansing and data profiling procedures. To arrange and control the data for the intended analytics purpose, more data preparation work is completed. The application of data governance regulations ensures that the data complies with corporate benchmarks and is used appropriately.
From there, a data scientist engages in the process of building an analytical model utilizing programming languages like Python, R, SQL, and Scala as well as other analytics software or predictive modeling tools. The model is often performed versus a biased data set to assess its accuracy at first, and then, when necessary, it is changed and tested again. The model is “trained” through this procedure until it functions as desired. The model is then tested in producing mode versus the overall data set, either once to meet a particular information requirement or repeatedly as the data is kept up to date.
Analytics applications may occasionally be set to initiate business actions automatically. One instance is stock trading conducted by a financial services company. Otherwise, the final stage in the data analytics method is to inform other end users and business executives about the outcomes generated by analytical models. To make results easier to grasp, additional infographics and charts can be designed. Data visualizations are frequently used in BI dashboard applications, which present data on a single screen and allow for data updates in real-time as new information is available.
Duties and Tasks of a Data Analyst
We shall examine the particular tasks and duties of Data Analysts in this section
Here Are the Tasks and Duties That Are Performed by a Data Analyst Within an Organization: –
Data analysts extricate data from a broad range of primary and secondary sources. They then arrange the very same data in a proper format that is simple and easy to read and understand.
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Maintenance of Databases:
Database systems are designed and maintained by data analysts. A database can be created, updated, read from, and deleted in this manner.
Preparation of Data:
Redundancy, errors, missing values, and many other issues would always exist with data gathered from various sources, indicating that the data seems to be in a raw format. Therefore, Data Analysts must fix data errors, eliminate unnecessary data, and recognize potential data to transform the raw data into arranged data after it has been extracted. To prepare the data for control and visualization by data scientists, they use a variety of data cleaning procedures.
Assurance of Data Quality:
The majority of companies are dependent on their data to carry out their daily operations. Therefore, attaining high-standard data is vital for increasing a company’s efficacy. Data analysts ensure that the data gathered from various sources is pertinent to the company’s business.
Collaboration with Other Teams:
For data scientists, other software development teams, and ML engineers, data analysts prepare the data. They are using the data to build automated software that is ML-based. Data analysts interact, coordinate, and work with development teams to provide essential data-related information.
Safeguarding the Privacy of Data:
A crucial resource for any organization is data and information. As a result, one of the key responsibilities of data analysts nowadays is to maintain data and information security.
Preparation of Reports
Reports that provide important information are prepared by data analysts. Charts and graphs are used in these reports to depict business-related elements. By examining factors like internal activities, market analysis, profitability, etc., they assist in tracking the course of business growth.
- Data Analysts help troubleshoot issues related to databases, information, reports, and, reports.
- Now let us move to the next section – skills that are needed to become a data analyst.
- Skills Required to Be a Data Analyst
For You to Be Qualified for Different Data Analyst Job Posts, You Must Focus on the Following Skills That a Data Analytics Expert Must Have: –
- Mathematical skills (especially probability statistics and statistics)
- For analyzing data, visualizing, and building machine learning models, solid programming skills in languages like Python and R are highly valued.
- Comprehensive understanding of database languages like SQL
- Knowledge of data visualization tools like Tableau, Power BI, or Qlik
- An understanding of how to prepare data using Microsoft Excel or Google Sheets
- Ability to gather standard data for analysis
- The amazing ability for problem-solving
- Skills for management of a project
Now I will brief you about the various advanced data analytics courses at XLRI. Data availability is no longer an issue because of the advancement of information technology. Working with these kinds of unorganized data to transform it into a useful interpretation that aids in better decisions for management is the focus of business analytics.
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Interested In Learning data Analytics? Download the course brochure to know if this course is for you/
1. Course Name: Advanced Post Graduate Certificate Business Data Analytics Courses at XLRI
XLRI has introduced a new certification course to meet the demands of the modern market. One year is all it takes for professionals to complete the advanced Post Graduate Certificate (PGC) Business Data Analytics Courses at XLRI, which offers the ideal foundation for learning analytics. The program’s main objective is to impart a comprehensive understanding of business analytics frameworks and methodologies and how to utilize them to make business decisions. Prescriptive analytics, Predictive analytics, and Descriptive analytics make up the three primary subfields of business analytics.
In a word, it involves applying statistics, computer programming, and operations research all at once to produce the desired result.
This Advanced Post Graduate Certificate (Pgc) Business Data Analytics Courses at XLRI Has Been Carefully Designed: –
- To aid analytics professionals in gaining managerial understanding through data analysis and comprehensive knowledge of numerous analytics fields
- To convert analytics professionals into multi-functional an amazing leader who is great at integrating all disciplines to use advanced analytics to fulfil the organization’s strategic goals
- To assist in addressing the intricacies and issues in the analytics field and provide analytics professionals with the skill sets they need to choose the best line of action.
The advantages of the Comprehensive Post Graduate Certificate (PGC) Business Data Analytics Courses at XLRI are as follows: –
- Chance to get an alumni status and a certificate of completion from XLRI
- Experience real-life uses of analytics across numerous fields
- Practical learning with the help of Microsoft Excel, Python, Minitab & R
- Detailed knowledge of Digital Media Analytics, Machine Learning, and Text Mining
- Three-Month Capstone Assignment
The syllabus of the Extensive Business Data Analytics Courses at XLRI is as follows: –
Business Analytics Tools
- Introducing R
- Introducing Python
- MS Excel (Spreadsheet) modeling & Power BI
Descriptive Statistics and Data Visualization
Multiple charts, graphs, and foundational statistical processes consisting of data properties
Statistics for Data Science
Fundamental Part analysis and more
- Linear Regressions Model
- Separate Option Models
- Logistic Regressions
- Quantile Logistic Regressions
- Probit Regressions
- Life Cycle of Data Analytics
- Abnormality Detection
- Interconnection Rule Learning
- Meta Analytics
- Neural Networks
- Several-layer Perceptron (SLP)
- Help Vector Machines (HVM)
- Closest Neighbour Algorithms (KCN)
- Introducing (AI) Deep learning and Artificial intelligence
- Information Recovery
- Lexical Examination
- Pattern Identification
- Information Extrication
- Natural Language Analysing (NLP)
Analytics Applications (Including a variety of uses in the below-mentioned domains)
- Supply Chain Management
- Digital Media Analytics
Prescriptive Analytics through Simulation and Optimization
- LP Simplex
- Evolutionary GRG and GRG using Excel Solver
- Demand Predicting and Management
- Uses of Time Series Models
Big Data Analytics
- Data Management in the era of Big Data
- Theory of Hadoop
- Data Stream Analytics
- Advice System etc
Three-Month Capstone Assignment
Eligibility to Attend the Rigorous Business Data Analytics Courses at XlRI
- Working experts in the fields of e-commerce, IT services, marketing, big data, research, etc., as well as individuals who wish to learn about modern, important, and applicable business analytics areas.
- Business analysts who want to learn more about the methods, concepts, and tools used in data analysis and data management
- Project managers, functional managers, and business leaders who manage vast, intricate databases and who must be outstanding at function-particular analytics
Eligibility for the Thorough Business Data Analytics Courses at XLRI is as follows:-
- Had mathematics or statistics as one of your subjects in Class 12 or graduated with at least 50% marks
- For Indian Student – Bachelors in Engineering (10+2+4) or Graduates in Science, Business or Commerce, Computers, (10+2+3) from a recognized university
- For Foreign Students – Graduation or equal degree from an accredited university or institute in their own countries
- At least one year of experience working in the field of analytics
- A thorough selection procedure is used to determine which participants will be admitted to the program. This process includes evaluating applicants’ academic records, employment history, and Statements of Purpose (SOP).
Pedagogy of the Extensive Business Analytics Course at XLRI
The main form of training will be provided through LIVE Online lectures that will be broadcasted online to student laptops or desktops through the Internet. The instructors at XLRI will convey their knowledge through case studies, lectures, teamwork, projects, analytical practical exercises, term papers, and assignments. Concepts will be clarified through examples taken from real-world situations. Additionally, all enrolled students will have access to the Cloud Campus, where they may find additional study tools, tests, and other reference materials as well as projects, case studies, and assignments as needed. Students will have the option to contact the instructors at any moment to ask questions and get answers to their uncertainties, either in-person during class or online through the Cloud Campus.
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2. Course Name – Executive Diploma Program (EDP) in Business Data Analytics Courses at XLRI for Managers
The goal of this advanced EDP in Business Data Analytics Course at XLRI is to give mid and senior managers the knowledge and skills they need to recognize the potential business benefits of using artificial intelligence and data science and to use those benefits in decision-making for management. Comprehending, utilising, and understanding data inside organizations is emphasized throughout the course. Participants would be able to do the following after completing this demanding EDP in Business Data Analytics Course at XLRI.
- Recognize the areas of their company where business analytics can be used profitably
- Have a managerial understanding of the techniques and tools used in AI, data science, and analytics
- Acquire knowledge and build skills for handling data analytics and data science teams
- Assist the organization in building an analytics team and a data-driven company
- Assess investment decisions taken for analytics projects in their companies
The Syllabus of the Advanced Edp in Business Data Analytics Courses at XLRI is as Follows:-
Module 1 – Analytics as Strategic Lever
- Data literacy and Data in organizations
- Forms of analytics – prescriptive, predictive, and descriptive
- Employ cases of Analytics – The presentation
- Bad and good Analytics
- Gleaning company’s data science goals from strategic business goals
- Building thesis for company’s decision-making – Utilize cases from numerous verticals
- Restriction of analytics – Where intuition still gains
- Case Study – Reading Harvard article on “Art of Persuasion and Data Science”
Module 2 – Reviewing Statistics for Managers
- Explaining and introducing data
- Utilizing central tendency measures while calculating business metrics
- Probability and its uses in business
- Sampling theses and their uses in business
- Thesis examination, employ cases in business
- Examination of variance, employ cases in business
- Regression and correlation employ cases in business
Module 3 – Decision making through Machine Learning Algorithms & Algorithms
- Major forms of algorithms
- Use of Multivariate and Multiple linear regressions models
- Classification of algorithms – Theory and employ cases
- Aggregating algorithms – Theory and employ cases
- Designing a recommender system
Module 4 – (Spreadsheet) Microsoft Excel Modeling for Business Decisions
- Modeling method of decision making
- Linear Programming method to solve business problems, for example
- Make-Buying decision
- Investing decision
- Blend issue
- Producing and inventory planning issue
- Numerous-period cash flow issue
- Delicate Examination and Simplex Process
Module 5 – Technology & Data Warehousing
- Querying and Data management
- Data extrication, conversion, and loading techniques
- Foundation of data warehouse
- Summary of the technology stack to assist analytics
- Module 6 – Digital Media Analytics
- Advantages and scope of digital media analytics
- Forms of digital media analytics
- Consumer insights through mobile and web application analytics
- Social media analytics
- Gist of sentiment examination and Text mining
- Online reputation management
Module 6 – Big Data
- Comprehending Big Data
- Comprehending the use of Big Data
- Big Data use tools – MapReduce, Hadoop
- Taking advantage of Big Data for business decision making
- Examples from Google, Google & Facebook
Module 7 – Use of Reinforcement Learning and Deep Learning in Decision Making for managerial purpose
- Foundation of Deep Learning
- Deep, Contortion, and Recurring Neural Network – Theory
- Employ cases of Deep Learning in various industry verticals
- Foundation of Augmented Learning
- Employ cases of Augmented Learning in the industry
- Module 9 – Data Visualizing and Story-telling
- The technique of story-telling with the help of data
- Data visualizing – use and concept
- Employing self-service data visualizing tools (for example Tableau) to produce dashboards and management reports
- Visualization of data with the help of info-graphics
- Assessing a report on analytics
Module 8– Handling a Gigantic-Scale Data Science Assignment
- Data Science project management technique and administration
- Use of Agile
- Handling organizational transformation
- Building and controlling a data science project contract
Module 9 – Building a Business Case for Analytics Assignment
- When is it appropriate to invest in data science and when is it not?
- Recognizing and calculating the advantages of data science for a company
- Forecasting investments in a data science Assignment
- Calculating the return on investment for a data science assignment
Module 10: Building a Data-Propelled Company
- New organization duties and administration for a data-propelled company
- Hiring and maintaining the talent pool for data science
- Building up a data team
- Vendor eco-system for data science services
Module 11 – Data Confidentiality and Trust
- The ethical issue of data confidentiality and trust within Analytics
- Techniques to conserve the confidentiality of delicate data
- Legal problems with data confidentiality
- Latest case studies
Module 12 – Capstone Project – The student will be given instructions to complete a capstone project for a field of industry of their preference.
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Who Should Attend the Vast Business Data Analytics Courses at XlRI
The goal of this advanced EDP in Business Data Analytics Course at XLRI is to give mid and senior managers the knowledge and skills they need to recognize the potential business benefits of using artificial intelligence and data science and to use those benefits in decision-making for management.
The pedagogy of the Advanced EDP in Business Data Analytics Courses at XlRI is as Follows: –
A very thorough program that is beneficial from the perspective of corporate management. The aforementioned goals will be achieved by the following:
- Assignment work
- Lecture Array
- Case methods
Frequently Asked Question
Q1. What is the role of a data analyst?
This was already covered in detail in the article, but now I am providing the same in short.
- Data mining
- Maintenance of databases
- Preparation of data
- Assurance of data quality
- Collaboration with other teams
- Safeguarding the privacy of data
- Preparation of reports
Q2. Can you explain to me the difference between the two business data analytics programs that are being provided by XLRI?
The PGC course covers the processes and frameworks of business analytics and how to employ them for decision-making. While the EDP in business analytics program will help you to comprehend, utilize, and understand how data is used inside an organization.
Q3. What is the duration of both courses?
The PGC course is for one year, while the EDP program lasts seven months.
So, these were the two best possible business data analytics courses at XLRI. You can choose any of the courses as per your work experience and convenience. Both of them are good programs on their own.