Power BI Data Analyst
The Power BI Data Analyst program provides students with a comprehensive introduction to the world of data analytics.
Power BI Data Analyst Training Program Description:
The Power BI Data Analyst program provides students with a comprehensive introduction to the world of data analytics. From collecting and cleaning data, to using advanced analytics tools such as Power BI to analyze it, this program will provide a comprehensive background on how to become an effective data analyst in today’s increasingly digital world.
During the course, students will learn how to manage, visualize and interpret big data from multiple sources. They will gain a deep understanding of core analytical concepts including linear regression, clustering, forecasting and others. In addition, they will learn about software development for analytics and develop practical skills that enable them to quickly think through business problems and provide insights.
With the help of our experienced trainers and mentors, students will gain the necessary skills to become competent and successful data analysts. After completing this program, students will be able to identify problems, analyze large datasets for insights, draw conclusions from their analysis, and present it in an effective manner.
This program is designed for those who want to utilize the power of data analytics in their profession or business. It is ideal for individuals with a technical background or aspiring professionals who are looking to upgrade their skillset and gain valuable experience in the field of data analytics. By enrolling in this Power BI Data Analyst program, you can build a successful career as a confident and reliable data analyst.
– Learn how to manage, visualize and interpret big data from multiple sources
– Understand core analytical concepts such as linear regression, clustering and forecasting
– Develop practical skills for problem solving in real business settings
– Build expertise in the use of Power BI for data analysis
– Gain insight into software development for analytics
– Acquire the necessary skills to become a confident and reliable data analyst.
Introduction to Data Analytics:
This module introduces students to the field of data analytics and the importance of using big data for decision making. Students will learn about the different types of data, their properties and how they are used in problem solving. They will also be introduced to basic statistical concepts such as averages, distributions and correlations.
Exploratory Data Analysis:
This module focuses on exploring datasets by generating descriptive summaries, visualizing patterns and uncovering relationships between variables. Students will learn various techniques such as plotting, clustering and hypothesis testing that can be used to better understand the dataset.
Power BI for Data Analysis:
In this module, students will gain hands-on experience working with Power BI Desktop to create interactive visuals and dashboards. They will learn to import data, create relationships between tables and use advanced analytics tools such as machine learning algorithms, natural language processing and predictive analytics.
Linear Regression & Machine Learning Techniques:
This module covers the fundamentals of linear regression and other supervised machine learning algorithms. Students will learn how to identify and train models for predicting outcomes on specific datasets. They will also explore the application of unsupervised techniques such as clustering and dimensionality reduction in various real-world scenarios.
Advanced Visualization & Dashboard Design with Power BI:
This module focuses on designing effective visuals that convey insights effectively. Students will gain an understanding of dashboard design principles and best practices for creating interactive dashboards using Power BI. They will learn to identify and integrate meaningful metrics, design visuals with impact and use storytelling techniques to communicate results.
Software Development for Analytics:
This module provides an introduction to software development for data analytics. Students will explore the basics of programming in Python, including variables, functions, control flow structures and loops. They will also learn about data wrangling and how it can be used for cleaning, transforming and analyzing datasets for insights.
Presentation & Communication of Results:
In this module, students will gain an understanding of the importance of effective communication of results from their analysis. They will learn various methods such as visualizations and presentations to effectively explain their findings to stakeholders. Additionally, they will develop skills in working with teams, clients and other stakeholders to create meaningful results.
At the end of this course, students will have the necessary skills to become confident and reliable data analysts who can make efficient decisions with their data. They will be equipped with a strong understanding of the fundamentals of data analytics as well as practical applications for problem solving in real business settings. The course also covers important topics such as software development for analytics, advanced visualization & dashboard design with Power BI and communication & presentation of results. By taking this course, students will gain the expertise they need to excel in the field of data analytics.
This program is available online, so you can learn at your own pace, on your own time.
Take advantage of this opportunity and invest in yourself today! Enroll now and become a Professional Power BI Data Analyst. We look forward to welcoming you into our program. Good luck!
Data Analysis: Process of inspecting, cleaning, transforming and modeling data with the goal of uncovering useful information, suggesting conclusions and supporting decision-making.
Power BI Desktop: A suite of business analytics tools to analyze data and share insights. It allows users to visualize their data in interactive reports and dashboards.
Linear Regression: Statistical technique used to model a linear relationship between an independent variable (x) and a dependent variable (y).
Machine Learning Algorithms: Set of algorithms that use mathematical models to enable computers to learn from past experiences or data in order to make predictions or decisions without being explicitly programmed.
Natural Language Processing (NLP): Subfield of artificial intelligence which deals with the interaction between computers and human (natural) languages.
Data Wrangling: Process of cleaning, transforming and mapping data in order to make it easier to work with.
Clustering: Type of unsupervised learning which is used to group similar objects together based on their characteristics or attributes.
Dimensionality Reduction: Technique used for reducing the number of features or variables in a dataset while preserving important information.
Dashboard Design Principles: Set of guidelines for creating effective visuals that convey insights effectively.
Storytelling Techniques: Methods used for communicating results from an analysis by using visualizations and presentations to explain findings to stakeholders.
Python Programming Language: High-level programming language designed for general-purpose programming. It is used in web development, software development, data science and machine learning.
Software Development: Process of creating, testing and maintaining computer programs or applications to meet the needs of users.
Data Visualization: Representation of data in graphical formats such as charts, plots and maps which can be used to explore patterns, trends and correlations from datasets.
Communication & Presentation of Results: Ability to effectively explain findings from an analysis using visualizations, presentations or other methods. This skill is vital when working with teams, clients and stakeholders.
Stakeholders: People or entities that are involved in or affected by a project’s outcome (e.g., customers, investors, employees).
Investment: Commitment of money or other resources to a specific project for the purpose of achieving a return.
Advanced Visualization & Dashboard Design with Power BI: Process of creating interactive visualizations and dashboards using Power BI’s tools. This skill is essential for data analysts in order to communicate their results effectively.
Deep Learning: Subfield of machine learning which uses large datasets and artificial neural networks to enable computers to ‘learn’ from examples without explicit programming instructions. It is used in various fields such as computer vision, natural language processing and robotics.
Data Mining: Process of exploring large datasets to identify patterns and trends that can help inform decisions.
Big Data Analysis: Use of powerful computer systems to analyze large and complex data sets. It involves techniques such as machine learning algorithms, natural language processing, clustering and dimensionality reduction.
Statistical Modeling: Process of using methods from statistics and mathematics to generate insights from data. It is used in a variety of applications such as forecasting demand, predicting outcomes or understanding customer behavior.
Predictive Analytics: Use of historical data to make predictions about future events or trends. Predictive analytics techniques are often used in marketing, insurance and healthcare applications.
Business Intelligence (BI): Set of technologies, tools and processes that organizations use to inform decisions and gain insights from data. It involves gathering, cleaning, organizing and analyzing data to uncover valuable business insights.
Cloud Computing: Use of web-based applications or services that are hosted on a remote server instead of in an organization’s own physical infrastructure. This allows businesses to save resources while still being able to access their data in a secure way.
Data Governance: Process of managing the quality, security and integrity of an organization’s data assets. Data governance practices are important for ensuring that companies have reliable sources of information which can be used to make informed decisions.
Automated Machine Learning (AutoML): Process of automating the entire machine learning workflow from feature engineering to model building and hyperparameter tuning. This type of technology helps organizations to quickly and efficiently build sophisticated models with minimal effort.
Data Science Ethics: Principles that guide decision-making in the context of data science projects, such as ensuring privacy and security, addressing bias, and being transparent about the data used. It is essential for companies to adhere to ethical standards when dealing with large datasets.
Data Visualization Tools: Software tools specifically designed for creating visual representations of data which are easy to understand and interpret. Examples include Tableau, QlikView, Microsoft Power BI and Google Charts.
Project Management & Delivery: Skills related to managing a project from start to finish, including planning, budgeting, execution and delivery. Good project management is essential for successfully managing data science projects.
Data Security: Practice of protecting an organization’s sensitive data from unauthorized access or misuse. It involves measures such as encryption, authentication and access control which help to protect data against malicious attacks.
AI & Machine Learning Governance: Practices used to ensure responsible use of artificial intelligence and machine learning technologies. This includes the management of accountability, transparency, privacy, security and fairness when dealing with large datasets.
Incentive Structures & Change Management Strategies: Processes used to incentivize individuals or teams in order to achieve desired outcomes. They are also important for effectively managing changes within an organization without causing disruption.
Power BI Data Analyst Associate: Microsoft certification program which covers the fundamentals of working with Power BI to analyze and visualize data. It includes topics such as accessing and preparing data, creating reports and dashboards, scalability considerations, data insights and more.
Microsoft Power BI Data Analyst: Role that focuses on analyzing data from various sources to gain meaningful insights for organizations. Responsibilities include creating effective visuals, identifying trends in the data, developing forecasts and building efficient models.
PL-300: Microsoft exam which is part of the Power BI Data Analyst Associate certification program. This exam focuses on a range of topics such as connecting to data sources, creating visuals with Power BI Desktop, transforming datasets and more.
Data Professionals: Individuals who specialize in managing large amounts of data and using their expertise to help organizations make informed decisions. This includes roles such as data engineers, data architects, data analysts and business intelligence professionals.
Data Analysis Using Power BI: Process of analyzing data from various sources with Microsoft’s Power BI suite of tools. It involves connecting to a variety of data sources, transforming the datasets into useful information, creating visuals to display the results and more.
Visualize Data: Process of taking large amounts of raw data and representing it in an easy-to-understand visual form, such as charts or graphs. This is done in order to quickly identify patterns in the datasets which can be used for decision making.
Scalable Data Models: Systems that allow data to be stored and accessed in a scalable way, meaning that the system can automatically adjust to changes in data size and complexity. This is important for organizations which need to store and process large amounts of data.
Data Professionals & Business Intelligence: Individuals who specialize in managing large amounts of data and using their expertise to help organizations make informed decisions. This includes roles such as data engineers, data architects, data analysts and business intelligence professionals.
Access & Process Data: Process of collecting, organizing and preparing datasets from various sources so that they can be analyzed. It involves connecting to different databases, cleaning the dataset, transforming the format of the information and more.
Prepare Data: Process of transforming raw datasets into useful information that can be used to generate insights and inform decision making. This includes activities such as cleaning the data, removing outliers, standardizing values and more.
Develop Reports: Process of creating documents or presentations which summarize and visualize the results from a data analysis project. This involves tasks such as selecting appropriate visuals based on the data, formatting the report in an efficient way and adding commentary to explain key points.
Data Insights: Information derived from analyzing large amounts of data which can help organizations make better decisions or gain a competitive edge in their industry. Examples include customer segmentation, predictive analytics and forecast models.
Microsoft certified: A certification from Microsoft which is earned by taking an exam and demonstrating knowledge in a specific area. Power BI Data Analyst Associate certification is one of the certifications offered by Microsoft.
Power BI Data Analyst Associate Certification: A certification offered by Microsoft that focuses on using Power BI to analyze data, develop reports, visualize information and gain insights. It includes topics such as connecting to data sources, creating visuals with Power BI Desktop and transforming datasets.
Data Analytics: An area of study which involves collecting and analyzing data to gain insights. It includes activities such as data cleansing, connecting to various databases, creating visuals from the dataset and more.
DAX: Acronym for Data Analysis Expressions. A language used in Power BI to create calculations and measures with an easy-to-use syntax.
Data Analytics: Process of exploring datasets in order to gain insight into the information contained within them. This involves tasks such as connecting to multiple data sources, querying the dataset, transforming the format of the information and visualizing it for easier interpretation.
PL-300 Exam: Microsoft exam related to Power BI Data Analyst Associate Certification which tests a candidate’s knowledge on topics like connecting to data sources, creating visuals with Power BI and transforming datasets.
Microsoft Azure: A cloud computing platform from Microsoft which provides services such as analytics, computing, networking and more. It allows organizations to move their workloads to the cloud in order to reduce costs and increase efficiency.
BI Consultant: An individual who specializes in Business Intelligence related activities such as developing solutions for data analysis projects or creating reports based on the results of a data analysis project.
BI Tools: Software applications used to analyze data and generate insights from it. Examples include Power BI, Tableau and Qlik Sense.