Enroll now to become a Certified Data Analytics expert with EDTIA Data Analytics Masters Program and upgrade your skills.
Data Analyst Course makes you experienced in tools and systems utilized by Data Analytics Professionals. It contains in-depth training in Statistics, Data Analytics with R and Tableau.
Data analytics certifications are prepared to demonstrate you know data management and analytics concepts and are proficient in data analysis skills. On the other hand, data analytics certificates and certificate programs are created to help you know more about the field.
A data science master's program helps you acquire skills to collect, manage and interpret data, its types, trends, and deliver the results accordingly. This developed skillset is extended out throughout the M.
There are no prerequisites for enrollment in the Data Analytics Course Masters Program.
experienced professional working in the IT industry, a candidate preparing to enter the world of Data Analyst
Data analysts work with enormous volumes of data, turning them into insights businesses can leverage to create better decisions. They work across various industries—from healthcare and finance to retail and technology.
Data analytics assists individuals and organizations make sense of data. Data analysts generally examine raw data for insights and trends. They utilize different tools and techniques to help organizations make decisions and succeed.
Skilled data analysts are some of the most sought-after experts in the world. Because the demand is so intense and the supply of people who can do this job well is restricted, data analysts control huge pay and excellent perks, even at the entry-level.
Data analytics is the science of examining basic data to make conclusions about that Information. The methods and procedures of data analytics have been automated into mechanical methods and algorithms that work over raw Data for human consumption. Data analytics assist a business in optimizing its performance.
Understand various data types, Various variable types, List the uses of varying types, Explain Population and Sample, Discuss sampling techniques, Understand Data representation
Understand rules of probability, dependent and independent events, Implement conditional, marginal, and joint probability using Bayes Theorem, Discuss probability distribution, Explain Central Limit Theorem.
Understand the concept of point estimation using confidence margin, Draw meaningful inferences using a margin of error, Explore hypothesis testing and its different levels
Understand the concept of association and dependence, Explain causation and correlation, Learn the idea of covariance, Discuss Simpson's paradox, Illustrate Clustering Techniques
Understand Parametric and Non-parametric Testing, various types of parametric testing, Discuss experimental designing, Explain a/b testing
Understand the concept of Linear Regression, Explain Logistic Regression, Implement WOE, Differentiate between heteroscedasticity and homoscedasticity, Learn the idea of residual analysis
learn important keywords in R like Business Intelligence, Business Analytics, Data, and Information, how R can play an essential role in solving complex analytical problems, use 'R' in the industry, compare R with other analytics software, and install R its packages.
Learn the basics of R programming, like data types and functions. This module presents a scenario and lets you think about the options to resolve it, such as which datatype should store the variable or which R function can help you in this scenario.
learn dirty data set and perform Data Cleaning on it, resulting in a data set ready for any analysis. Thus utilizing and exploring the popular functions needed to clean data in R.
comprehend the versatility and robustness of R, which can take up data in different formats, from a csv file to the data scratched. This module teaches you different data importing techniques in R.
learn that exploratory data analysis is an essential step in analyzing various tasks involved in a typical EDA process.
learn that Visualization is the USP of R. You will learn the concepts of creating complex and straightforward visualizations in R.
comprehend the various Machine Learning algorithms. The two Machine Learning types are Supervised Learning and Unsupervised Learning, and the distinction between the two types, discuss the process involved in 'K-means Clustering' and the various statistical measures you require to know to execute it in this module.
know how to see the associations between many variables utilizing the popular data mining technique called the "Association Rule Mining" and execute it to predict buyers' next purchase. Learn a new design that can be utilized for recommendation purposes called "Collaborative Filtering." Different real-time-based scenarios are shown utilizing these techniques.
Learn the base of 'Regression Techniques.' Linear and logistic RegressionRegression is explained from the basics with the examples, and it is implemented in R using two case studies dedicated to each type of RegressionRegression discussed.
Learn about the Analysis of Variance (Anova) Technique. The algorithm and various aspects of Anova have been discussed in this module, Sentiment Analysis, and how we can fetch, extract, and mine live data from Twitter to find out the sentiment of the tweets.
Learn concepts of Decision Trees and Random Forest. Random Forests algorithm is discussed step-wise and explained with real-life examples. These concepts are implemented on a real-life data set at the end of the class.
discusses different concepts taught throughout the course and their execution in a project.
Get a brief idea of the Data Visualization and Tableau Prep Builder tool.
get a brief idea of Tableau UI components and various ways to establish a data connection.
comprehend the significance of Visual Analytics and analyze the diverse charts, features, and techniques used for Visualization.
understand basic calculations such as Numeric, String Manipulation, Date Function, Logical and Aggregate, Table Calculations, and Level Of Detail (LOD) expressions.
deep dive into Visual Analytics in a more fine manner. It covers different advanced methods for analyzing data, including Forecasting, Trend Lines, Reference Lines, Clustering, and Parameterized concepts.
deep dive into advanced analytical scenarios, utilizing Level Of Detail expressions.
understanding of Geographic Visualizations in Tableau.
learn to plot various advanced graphs in Tableau Desktop.
know to create Dashboards and Stories within Tableau.
discover effective ways of creating Dashboards with minimum time investment.
know to publish data, interact, modify, and secure the issued Data on Tableau Online.
know to produce Tableau reports for different industrial scenarios and publish them on Tableau Online. Understand to manage permissions and secure data utilizing filters.
Learn building blocks and the various fundamental concepts of Power BI.
know how to extract data from various sources, establish connections with Power BI Desktop, perform transformation operations on data, and the Role of Query Editor in Power BI.
learn the basics of DAX in Power BI Desktop.
comprehend the advantages and best practices of Data Visualization. It will also assist you in making charts utilizing Custom Visuals.
Learn to create Dashboards and publish them on Power BI services. Know how to monitor Real-time Data with REST API.
learn how to connect data sources directly to Azure, HD Spark, and My SQL and create interactive dashboards.
comprehend Power BI Report Servers and data gateways. Learn about the web portal where you display and manage reports and KPIs.
learn to create R and Python visuals in Power BI Desktop and Power BI Service.