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Overview of Power BI ecosystem and Fundamentals & Limitations of Power BI and Types of Softwares Using For Reporting and its Introduction.
Installation, Power BI Desktop interface, and basic navigation of Each Component in Power Bi Desktop and Building Blocks of Power BI"
Power Query basics and Important sections in Power Query and data transformation
Data sources in Power BI, importing data into Power BI Desktop and Power Query Transformations with Inbuilt Row transformations and Inbuilt Column transformations.
Custom Column Creation in Power Query and Differences between Transform and Add Column Tab.
Parameters, Types of Parameters and Creation and Invoking of Query Parameters in Power Query.
Relationships Between Tables
Types of Tables, Keys and Cardinalities and Differences between them.
Types of Data Connectivity Modes and Differences Between Them and When to Use Assume Referential integrity.
Schemas, Types of Schemas and When to use which and Conversion of One schema to another.
Introduction for advanced data model with DAX and Differences between Calculated columns, Measures and Tables.
Text Functions, Logical Functions, Date & Time functions and creation of Date Dimension table, Math & Statistical Functions.
Filter Functions and differences between them.
Time intelligence functions and implementation of them in Power BI Visuals
Advanced Visualization Techniques like Interactions.
Filters in Power View and types of filters, Drillthrough, Slicers and Sync slicers
Types of Visuals explanation with Purpose, Field wells and Formatting options of each.
Building Reports - Visualization best practices and Custom Visuals, AI visuals and Publishing the Report to Power BI Service.
Publishing to Power BI Service.
Managing datasets in the Power BI
Creation of Dashboards and Dashboard settings and Sharing and Collaboration - Sharing reports and dashboards.
Setting up and managing workspaces and types of Workspaces and differences Between them.
Power BI Service Advanced Features like Embedding the report in Web and Public.
Power BI Apps and Data Refresh and Scheduled Refresh using Data Gateways.
RLS and Implementation of RLS(Row Level Security) and types of RLS
Incremental Refresh and types of refresh in Power BI and Differences Between them.
Power BI Best Practices - Optimization tips for performance, Security and access control.
Troubleshooting and Maintenance - Common issues and solutions and Upgrades and updates Practical Applications and Case Studies
Analysing real-world scenarios using Power BI and Hands-on exercises and projects and Project work and group discussions.
Review and feedback.
Addressing Technical and scenario based Questions.
After the 16 Weeks of Training Program, Candidates will undergo an assessment to determine their suitability for Working on live Projects.
1. Comprehensive test covering all aspects of the Power BI training.
2. Candidates who do not pass the test will be placed into a Performance Improvement plan(PIP).
3. A refresher training for PIP will be held over a period of 15 days.
4. After a 15-day period, Candidates in PIP will undergo retest.
5. Those who clear PIP are transitioned to projects.
6. Candidates who do not successfully complete the PIP will be required to exit the office.
1. Mentorship Q&A Sessions:
2. Regular sessions to address queries and Provide Guidance by trainer or TL.
3. Hands-on Exercises:
4. Hands-on activities for Power BI.
Our Power-BI training has precisely been developed to reach out to the demand of the learners with keeping in mind the industry standards. This DevOps course will particularly be helpful for the career advancement of the following audience
Graduates from College.
Currently working employees looking to upskill themselves.
Candidates looking for a change in the IT Field.
As such, there are no specific prerequisites for Power-BI Training in Hyderabad. If you are familiar with programming and foundation skills with a sense of curiosity and willingness to learn you are all set for the Power-BI training.
Power-BI Training Classes are conducted over the Weekdays and Weekends through classroom and online sessions. Please get in touch with theEdueo team to get exact schedule and timings.
Power-BI Course duration is 2 Months
Weekday Power-BI Training classes will be one hour long and Weekend classes will be three hours long.
Please find the detailed Power-BI course curriculum in the Edueo Power-BI training curriculum section.
Yes, we will assist our students with all the interview preparation techniques.
Edueo is the best environment for upskilling and skilling your career. I've joined for DevOps training over here. The trainer was way too good! Explained everything with detailed real time examples. It has a very good infrastructure. One of the best DevOps training institutes in Hyderabad.
I had completed the DevOps Course in Edueo. The DevOps training was well organized and structured. I really liked the trainer’s approach. Very supportive and relaxed. His approach has benefited me in terms of confidence. An empowering course – very encouraging with a lot of practical work. The study material, exercises and case studies provided were very good. Working on these case studies will help you a lot to with learning the topics. I would definitely recommend Edueo.
Best DevOps Training in Hyderabad with hand on Labs and great exposure to all the other Technologies .Best platform to learn DevOps course.
I took DevOps training in Hyderabad. Edueo is the best institute if you want to make yourself better. The best part is, the classes won't be like someone standing there and giving lectures, there is a lot of interaction between the trainers and the students.
After Taking Devops Training in Hyderabad , I got placed in as a DevOps Engineer.Edueo has helped me a lot to unleash my potential.
It has been a very good school of learning, trainer explains every individual concept (Theory/Practical) in-depth when I joined for DevOps training at the Gachibowli branch Hyderabad. It has an extraordinary infrastructure with un-interrupted broadband. Your doubts can be clarified with live projects. Altogether it was an awesome experience learning in Edueo.
Best place to learn DevOps course in hyderabad, the trainer is well knowledged and trains in depth. Practice sessions helped me a lot and the infrastructure is superb compared to other places. I have cracked my certification exam in DevOps training course with the help of the training here.
I have finished my Devops training in hyderabad from Edueo. I got the job right after the training. I'm very happy. You should definitely check out Edueo to elevate your career prospects.
Build an XML to CSV converter using Python
This python project which will help students to brush up their basic python skills to build a real-world XML to CSV Converter.
Industry: Data Operations
Tools: Python, XML, CSV, Data Ingestion
Building a Photo Editor from scratch using Flask and Numpy
Numpy is a versatile package to do data operations on matrices, numbers and number operations. With this project we will learn doing number operations on Images and apply filters, cropping, flipping and resizing using Numpy.
Industry: Media and Photography
Tools: Python, Numpy, Image Processing, Matplotlib
Exploratory Data Analysis on Retail Shop Sales data.
Performing exploratory data analysis to find patterns in data which will determine the approach to take in Machine Learning. Exploratory data analysis will help identifying features which can be used or discarded in the Machine Learning approach.
Industry: Retail
Tools: Exploratory Data Analysis, Data Preprocessing, Scaling Techniques, Missing Value Imputations.
Fruit type prediction using K Nearest Neighbors algorithm
K Nearest Neighbor algorithm is used to predict the type of fruit given its mass, height, width and color score. We will understand on how to use KNN algorithm and how to tune the hyperparameters such as n_neighbors and k value.
Industry: Food and Beverages
Tools: Supervised Machine Learning, K Nearest Neighbor Classifier, KNN Algorithm, Pandas, Numpy, Matplotlib
Predict Malignancy in Mammographic Masses using Decision Tree Classifiers
Mammographic masses is a public dataset from UCI machine learning repository which has information on mass shape, structure, density and age of a person. With this project we will use Decision Tree classifier to detect Malignancy (either Malignant or Benign) of the Mammographic Mass
Industry: Healthcare
Tools: Decision Tree Classifier, Sklearn, Pandas, Numpy, Graphviz, Matplotlib
Predict whether a candidate will be shortlisted in H1B Visa process using Random Forest Algorithm.
Every year there are close to 1 lakh 65 thousand applications for H1B Visa processing and only few get shortlisted in the process. A lot of information is used for scrutinizing candidates in the selection process. In this project we will use Random Forest algorithm on past five years of H1B processing data to identify if a H1B application will be selected or rejected in the application process.
Industry: US H1B Visa - USCIS
Tools: Random Forest, Pandas, Sklearn, Numpy, Seaborn, Matplotlib
Predict Breast Cancer using Support Vector Machine algorithm.
Wisconsin Breast Cancer dataset has 569 sample of Breast cancer observations determining Malignancy or Benign state of breast mass. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. In this project we will use SVM algorithm to detect malignancy in the cell.
Industry: Healthcare
Tools: SVM, Numpy, Pandas, Sklearn
HR Analytics for Attrition Prediction using Logistic Regression
Employee Attrition is an important subject to gauge the satisfaction of the employee in a company. HR departments take various measures to arrest employee attrition. In this project we will use Logistic regression to predict who is the potential employee who is in a verge of leaving the company.
Industry: Human Resources
Tools: Logistic Regression, Sklearn, Numpy, Pandas, Matplotlib
Churn Analysis in Telecommunication:
A customer can be called as a “churner” when he/she discontinue their subscription in a company and move their business to a competitor. Prediction as well as prevention of customer churn brings a huge additional revenue source for every business.
Here, we use a telecom customer data set to classify the set of possible customers who are likely to churn
Industry: Telecommunications
Tools: SVM, K Nearest Neighbors Classifier, Random Forest, Logistic Regression, Pandas, Numpy, Sklearn, Seaborn, Matplotlib
Predicting Housing prices using Regression
Description: Predict the sales price for each house based on input features provided for the house.
Industry: Real Estate
Tools: Linear Regression, Multi Linear Regression, Sklearn, Pandas, Numpy, Matplotlib
Social Network Ads based Prediction
Description: Predicting if a user buys a specific product or not based on the ad populated on the Social Network. Data uses specific demographic information about the user to do the prediction.
Industry: Social Media
Tools: Hierarchical Clustering, Scipy, Sklearn, Pandas, Numpy, Seaborn
Retail Customer segmentation based on spending patterns
Description: Customer analysis plays a crucial role in determining the profitability of Retail companies. Segmentation of the customers based on their purchase patterns helps Retail companies to cluster their user base and serve them effectively.
Industry: Retail
Tools: K-Means, Clustering, Sklearn, Pandas, Numpy, Seaborn
Market Basket Analysis using Apriori Algorithm
Market Basket Analysis is a technique which identifies the strength of association between pairs of products purchased together and identify patterns of co-occurrence. A co-occurrence is when two or more things take place together.
The technique determines relationships of what products were purchased with which other product(s).
Industry: Retail
Tools: Apriori Algorithm, Numpy, Pandas
SMS Spam Detection using Natural Language Processing
In our day-to-day lives, we receive a large number of spam/junk messages either in the form of Text (SMS) or E-mails. It is important to filter these spam messages since they are not truthful or trustworthy.
In this case study, we apply various machine learning algorithms to categorize the messages depending on whether they are spam or not.
Industry: Social Media
Tools: NLP, Sklearn, Logistic Regression
Sentiment analysis on Restaurant Reviews using Natural Language processing and Supervised Learning
It involves in identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral.
Industry: Food and Beverages, E-Commerce
Tools: NLP, Sklearn, Support Vector Machine
Image Classification using Deep Learning
Classifying Images based on the features is a tough problem. With Deep Learning algorithms like CNN it has become fairly easy. In this project we will learn to classify two faces using Convolutional Neural Networks.
Industry: Media
Tools: Deep Learning, Neural Networks, Tensorflow, Keras, CNN
Content Based Recommender Engine using Deep Learning
Content based recommender systems use the content in the data to segment items and then use them for recommending similar items. In this project we will use Deep Learning and Convolutional Neural Networks to perform content-based recommender engine.
Industry: E-Commerce, Retail
Tools: Tensorflow, Keras, CNN, Transfer learning, Deep Learning
Chatbots using Recurrent Neural Networks and Deep Learning
A chatbot is an artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone. In this project we will build a conversational chatbot using Deep Learning.
Industry: Retail, Banking, E-Commerce, Media
Tools: Tensorflow, Keras, RNN, LSTM, Numpy
Stock market price prediction
This project deals with the predictions of stock market prices using history of Data. It also considers the physical factors vs. psychological, rational and irrational behavior etc. Machine learning techniques implemented in Python acts as game changer for the predictions. Algorithms including Linear regression, LSTM and ARIMA model are used for the same.
Industry: Finance
Tools: Tensorflow, Keras, LSTM, RNN
Exploratory Data analysis of Crime records in Boston
This project analyses data using quantitative prediction of crimes in Boston and drawing visualizations of Trends in the data over the years. Exploratory Data Analysis is carried on the crimes data using lots of techniques from Linear model to Stochastic gradient boosting.
Industry: Defense and Law Enforcement
Tools: Linear Regression, XGBoost, Numpy, Sklearn, Pandas, Matplotlib
