FreeCourse Logo
FreeCourse.io
Verified CouponsFree CoursesJobsBlog
Categories
Home/Courses/Data Analysis - Business Intelligence | Python | Pandas |SQL
Data Analysis - Business Intelligence | Python | Pandas |SQL
Development100% OFF

Data Analysis - Business Intelligence | Python | Pandas |SQL

Udemy Instructor
4(12.8K students)
Self-paced
All Levels

About this course

In business, being able to understand, harness, and use data is no longer a skill reserved for a handful of well-paid analysts. It's becoming an essential part of many roles. If that sounds daunting, don't worry.

There is a growing set of tools designed to make data analysis accessible to everyone, in this huge-value, four-course Data Analysts Toolbox bundle we look in detail at three of those tools: Excel, Python, and Power BI. In isolation Excel, Python, and Power BI are useful and powerful. Learn all three and you are well on your way to gaining a much deeper understanding of how to perform complex data analysis.

This Data Analysts Toolbox bundle is aimed at intermediate Excel users who are new to Python and Power BI. All courses include practice exercises so you can put into practice exactly what you learn. What Can SQL do?

SQL can execute queries against a databaseSQL can retrieve data from a databaseSQL can insert records in a databaseSQL can update records in a databaseSQL can delete records from a databaseSQL can create new databasesSQL can create new tables in a databaseSQL can create stored procedures in a databaseSQL can create views in a databaseSQL can set permissions on tables, procedures, and viewsPower BIWhat is Power BI and why you should be using it. To import CSV and Excel files into Power BI Desktop. How to use Merge Queries to fetch data from other queries.

How to create relationships between the different tables of the data model. All about DAX including using the COUTROWS, CALCULATE, and SAMEPERIODLASTYEAR functions. All about using the card visual to create summary information.

How to use other visuals such as clustered column charts, maps, and trend graphs. How to use Slicers to filter your reports. How to use themes to format your reports quickly and consistently.

How to edit the interactions between your visualizations and filter at visualization, page, and report level.

Skills you'll gain

Data ScienceEnglish

Available Coupons

Loading...

Course Information

Level: All Levels

Suitable for learners at this level

Duration: Self-paced

Total course content

Instructor: Udemy Instructor

Expert course creator

This course includes:

  • 📹Video lectures
  • đź“„Downloadable resources
  • 📱Mobile & desktop access
  • 🎓Certificate of completion
  • ♾️Lifetime access
$0$94.99

Save $94.99 today!

Enroll Now - Free

Redirects to Udemy • Limited free enrollments

Share this course

https://freecourse.io/courses/data-analysis-business-intelligence-python-pandas-sql

You May Also Like

Explore more courses similar to this one

Neural Signal Processing & Applied AI
Development
0% OFF

Neural Signal Processing & Applied AI

Udemy Instructor

“This course contains the use of artificial intelligence”Neural Signal Processing with AI is a comprehensive, hands-on course designed to help learners master the analysis of neural and brain signals using modern Artificial Intelligence (AI) and Machine Learning (ML) techniques. This course bridges the gap between traditional signal processing and data-driven AI models, making it ideal for students, researchers, and professionals interested in EEG analysis, brain-computer interfaces (BCI), healthcare analytics, and applied AI.You will begin with a strong foundation in neural signal fundamentals, including how neural data is generated, recorded, and interpreted. Early sections focus on signal acquisition, sampling, noise characteristics, and ethical considerations. Each section includes a hands-on lab, where you will work with real or simulated neural datasets to reinforce theoretical concepts.The course then dives into core signal processing techniques, such as filtering, artifact removal, time-domain and frequency-domain analysis, and feature extraction. Through guided labs, you will implement these methods using Python-based tools and libraries, preparing neural data for intelligent modeling.Next, you will explore machine learning models for neural data, including classical classifiers, deep neural networks, CNNs, RNNs, and transformer-based architectures. Dedicated labs in each section will walk you through model training, evaluation, and performance optimization on neural signals.Advanced sections cover calibration-free learning, transfer learning, subject-independent models, and real-time neural processing pipelines. You will build end-to-end systems that transform raw neural signals into actionable outputs, with hands-on labs integrating AI models into real-time or simulated applications.Finally, the course addresses ethics, reliability, experimental design, and research-level best practices, ensuring you can build robust, reproducible, and responsible AI systems for neural data.By the end of this course, you will have practical experience across every stage of the neural AI pipeline, supported by hands-on labs in every section, and be fully equipped to apply AI to real-world neural signal challenges.

0.0•2.6K•Self-paced
FREE$101.99
Enroll
Machine Learning Essentials: Build Intelligent Models
Development
0% OFF

Machine Learning Essentials: Build Intelligent Models

Udemy Instructor

Machine Learning is one of the most in-demand skills in today’s tech industry. From recommendation systems and fraud detection to image recognition and predictive analytics, machine learning powers many of the intelligent systems we use every day.Machine Learning Essentials: Build Intelligent Models is designed to give you a strong, practical foundation in machine learning. This course focuses on understanding core concepts and applying them through hands-on model building, rather than just theory.You’ll start by learning how machine learning works, why it’s used, and where it fits within the broader field of data science and artificial intelligence. As the course progresses, you’ll build, train, and evaluate machine learning models using real datasets, helping you gain confidence in applying ML techniques to real-world problems.Whether you’re a student, developer, or professional looking to upskill, this course will help you understand machine learning clearly and practically.Skills You’ll GainAbility to build and evaluate intelligent machine learning modelsStrong understanding of core ML terminology and workflowsPractical experience applying machine learning conceptsConfidence to continue into advanced ML or AI topicsWhy Take This Course?Clear, beginner friendly explanationsHands-on learning with practical examplesFocus on building real, intelligent modelsSolid foundation for advanced machine learning topicsBy the end of this course, you’ll have a clear understanding of machine learning essentials and the ability to build intelligent models with confidence.

0.0•2.1K•Self-paced
FREE$99.99
Enroll
Machine Learning & Predictive Modeling: Practice Exams
Development
0% OFF

Machine Learning & Predictive Modeling: Practice Exams

Udemy Instructor

Having a massive dataset is useless if you cannot extract predictive value from it. Welcome to the Machine Learning & Predictive Modeling practice assessments! In the modern business analytics ecosystem, companies do not just want to know what happened in the past; they want algorithms that predict what will happen next. This comprehensive practice test course provides you with 200 expertly crafted, highly unique practice questions designed to simulate the rigorous technical assessments given during data science engineering interviews.Across these four rigorous practice exams, you will be thrown into high-stakes algorithmic scenarios. You will test your ability to train house price prediction regression models using Kaggle datasets, build deep learning customer churn models using TensorFlow and Keras, and develop complex energy efficiency regression models. The questions push you to evaluate deep mathematical trade-offs: When should you prioritize Recall over Precision? Why does a Random Forest handle non-linear data better than a standard Logistic Regression? How does Dropout regularization prevent a neural network from overfitting?Every single question in this course is unique and includes a detailed explanation of the "why" behind the correct algorithmic approach. By reviewing these explanations, you will learn industry-standard methodologies for Hyperparameter Tuning (GridSearchCV) and preventing catastrophic data leakage. If you are preparing for a career as a Data Scientist, refining your predictive models, or aiming to dominate Kaggle competitions, this is your ultimate testing ground. Enroll today and train your model!Course locale: English (US) Course instructional level: Intermediate Level Course category: Development Course subcategory: Data Science

0.0•403•Self-paced
FREE$98.99
Enroll
FreeCourse LogoFreeCourse

Freecourse.io brings you high-quality online courses with free certificates to help you upskill, boost your career, and achieve your goals anytime, anywhere.

Resources

  • Courses
  • Jobs
  • Categories
  • Features

Company

  • About
  • Blog
  • Contact

Legal

  • Privacy
  • Terms
  • Cookies
  • Licenses

© 2026 FreeCourse. All rights reserved.