| 001 Introduction.mp4 | 3.8 MB | ||
| 001 Kaggle Datasets.mp4 | 26.3 MB | ||
| 001 Office 365 setup ( Optional).mp4 | 71.4 MB | ||
| 001 What is Power BI.mp4 | 35.3 MB | ||
| 001 What is Tableau.mp4 | 21.2 MB | ||
| 002 Activating office 365 ( Optional).mp4 | 16.9 MB | ||
| 002 Tableau Data Sources.mp4 | 11.7 MB | ||
| 002 Tabular data.mp4 | 28.9 MB | ||
| 002 What is Power BI Desktop.mp4 | 11.7 MB | ||
| 002 What is Python.mp4 | 16.8 MB | ||
| 003 Exploring Pandas DataFrame.mp4 | 10.2 MB | ||
| 003 Installing Power BI Desktop.mp4 | 37.5 MB | ||
| 003 Logging into office 365 (Optional).mp4 | 27.6 MB | ||
| 003 Tableau File Types.mp4 | 16.6 MB | ||
| 003 What is Jupyter Notebook.mp4 | 4.6 MB | ||
| 004 Analysing and manipulating pandas dataframe.mp4 | 53.4 MB | ||
| 004 Installing Jupyter Notebook Server.mp4 | 30.5 MB | ||
| 004 Power BI Desktop tour.mp4 | 37.4 MB | ||
| 004 Tableau Help Menu.mp4 | 6.7 MB | ||
| 004 What is Power Pivot.mp4 | 2.9 MB | ||
| 005 Connect to a data source.mp4 | 17.9 MB | ||
| 005 Office versions of power pivot.mp4 | 6.9 MB | ||
| 005 Power BI Overview_ Part 1.mp4 | 23.1 MB | ||
| 005 Running Jupyter Notebook Server.mp4 | 42.3 MB | ||
| 005 What is data cleaning.mp4 | 10.1 MB | ||
| 006 Basic data cleaning.mp4 | 118.1 MB | ||
| 006 Common Jupyter Notebook Commads.mp4 | 28.5 MB | ||
| 006 Enable Power Pivot in excel.mp4 | 7.3 MB | ||
| 006 Join related data sources.mp4 | 55.2 MB | ||
| 006 Power BI Overview_ Part 2.mp4 | 25.9 MB | ||
| 007 Data Visualization.mp4 | 14.1 MB | ||
| 007 Join data sources with inconsistent field.mp4 | 36 MB | ||
| 007 Jupyter Notebook Components.mp4 | 21.9 MB | ||
| 007 Power BI Overview_ Part 3.mp4 | 41.1 MB | ||
| 007 What is Power Query.mp4 | 13.7 MB | ||
| 008 Components of Power BI.mp4 | 8.6 MB | ||
| 008 Connecting to a data source.mp4 | 31.3 MB | ||
| 008 Data Cleaning.mp4 | 44.3 MB | ||
| 008 Jupyter Notebook Dashboard.mp4 | 21.8 MB | ||
| 008 Visualizing qualitative data.mp4 | 49.6 MB | ||
| 009 Building blocks of Power BI.mp4 | 42.4 MB | ||
| 009 Exploring Tableau interface.mp4 | 30 MB | ||
| 009 Jupyter Notebook Interface.mp4 | 16.6 MB | ||
| 009 Preparing query.mp4 | 45.8 MB | ||
| 009 Visualizing quantitative data.mp4 | 62.8 MB | ||
| 010 Cleansing data.mp4 | 73.8 MB | ||
| 010 Creating a new Jupyter Notebook.mp4 | 20.5 MB | ||
| 010 Exploring Power BI Desktop Interface.mp4 | 30.6 MB | ||
| 010 Reorder fields in visualization.mp4 | 28.5 MB | ||
| 011 Change Summary.mp4 | 16.3 MB | ||
| 011 Enhancing query.mp4 | 79 MB | ||
| 011 Exploring Power BI Service.mp4 | 22.2 MB | ||
| 012 Creating a data model.mp4 | 48.2 MB | ||
| 012 Power BI Apps.mp4 | 22.5 MB | ||
| 012 Split text into multiple columns.mp4 | 18.4 MB | ||
| 012 us_baby_names.csv | 42.3 MB | ||
| 013 Building data relationships.mp4 | 41.1 MB | ||
| 013 Connecting to web data.mp4 | 26.7 MB | ||
| 013 Presenting data using stories.mp4 | 28.9 MB | ||
| 014 Clean and transform data _ Part 1.mp4 | 37.9 MB | ||
| 014 Create lookups with DAX.mp4 | 40 MB | ||
| 015 Analyse Data with Pivot Tables.mp4 | 50.8 MB | ||
| 015 Clean and transform data _ Part 2.mp4 | 78 MB | ||
| 016 Analyse data with Pivot Charts.mp4 | 52.5 MB | ||
| 016 Combining Data Sources.mp4 | 44.2 MB | ||
| 017 Creating Visualization _ Part 1.mp4 | 29.9 MB | ||
| 017 Refresh Source Data.mp4 | 49.2 MB | ||
| 017 listings.csv | 6.7 MB | ||
| 018 Creating Visualization _ Part 2.mp4 | 35.3 MB | ||
| 018 Update Queries.mp4 | 67 MB | ||
| 019 Create new reports.mp4 | 43.8 MB | ||
| 019 Publishing Reports to Power BI Service.mp4 | 29.7 MB | ||
| 020 Importing and transforming data from Access db file.mp4 | 79.2 MB | ||
| 021 Changing locale.mp4 | 10.6 MB | ||
| 022 Connecting to MS Access DB File.mp4 | 38.8 MB | ||
| 023 Power query editor and queries.mp4 | 40.6 MB | ||
| 024 Creating and managing query groups.mp4 | 27.3 MB | ||
| 024 Financial+Sample.xlsx | 81.5 KB | ||
| 025 Renaming Queries.mp4 | 32.3 MB | ||
| 026 Splitting Columns.mp4 | 41.1 MB | ||
| 027 Changing Data Types.mp4 | 38.5 MB | ||
| 028 Removing and reordering columns.mp4 | 51.6 MB | ||
| 029 Duplicating and adding columns.mp4 | 24.4 MB | ||
| 030 Creating conditional columns.mp4 | 46.3 MB | ||
| 031 Connecting to files in folder.mp4 | 47.1 MB | ||
| 032 Appending queries.mp4 | 45.6 MB | ||
| 033 Merge queries.mp4 | 36.8 MB | ||
| 034 Query dependency view.mp4 | 28.8 MB | ||
| 035 Transform less structured data_ Part 1.mp4 | 55.1 MB | ||
| 036 Transform less structured data_ Part 2.mp4 | 59.3 MB | ||
| 037 Creating tables.mp4 | 15.8 MB | ||
| 038 Query Parameters.mp4 | 49 MB | ||
| 054 Multi-Level-Spreadsheet.xlsx | 8.7 KB | ||
| 057 SalesByCountry.xlsx | 32.2 KB | ||
| 065 EV+sales+(King+county).csv | 27.6 MB | ||
| 066 Prep.xlsx | 14.8 MB | ||
| 067 Cleansing.xlsx | 14.8 MB | ||
| 068 Enhance.xlsx | 13.4 MB | ||
| 069 PrepPP.xlsx | 13.9 MB | ||
| 070 AddData.xlsx | 14.8 MB | ||
| 071 Lookups.xlsx | 14.8 MB | ||
| 072 Pivot.xlsx | 15.4 MB | ||
| 073 Charts.xlsx | 17.3 MB | ||
| 074 Refresh.xlsx | 17.3 MB | ||
| 075 Update.xlsx | 17.3 MB | ||
| 081 SampleData.xlsx | 485 KB | ||
| 082 JoinExamples.xlsx | 294.7 KB | ||
| 083 DifferentNames.xlsx | 294.8 KB | ||
| 084 CleanData.xlsx | 484.7 KB | ||
| 088 Split.twbx | 95.2 KB | ||
| 089 Storylines.twbx | 518.6 KB | ||
| CA Sales.csv | 2.7 MB | ||
| DE Sales.csv | 8.6 MB | ||
| Downloaded from 1337x.html | 512 B | ||
| FR Sales.csv | 13.6 MB | ||
| MX Sales.csv | 7.6 MB | ||
| PowerBI.accdb | 770.7 MB | ||
| ▲ 117 total files | |||
Knowledge should not be limited to those who can afford it or those willing to pay for it. If you found this course useful and are financially stable please consider supporting the creators by buying the course :)
Data Analysis & Visualization: Python | Excel | BI | Tableau
Connect to data, clean & transform data, analyse and visualize data.
Original Price: CA$54.99
Description
As a data analyst, you are on a journey. Think about all the data that is being generated each day and that is available in an organization, from transactional data in a traditional database, telemetry data from services that you use, to signals that you get from different areas like social media.
For example, today's retail businesses collect and store massive amounts of data that track the items you browsed and purchased, the pages you've visited on their site, the aisles you purchase products from, your spending habits, and much more.
With data and information as the most strategic asset of a business, the underlying challenge that organizations have today is understanding and using their data to positively effect change within the business. Businesses continue to struggle to use their data in a meaningful and productive way, which impacts their ability to act.
The key to unlocking this data is being able to tell a story with it. In today's highly competitive and fast-paced business world, crafting reports that tell that story is what helps business leaders take action on the data. Business decision makers depend on an accurate story to drive better business decisions. The faster a business can make precise decisions, the more competitive they will be and the better advantage they will have. Without the story, it is difficult to understand what the data is trying to tell you.
However, having data alone is not enough. You need to be able to act on the data to effect change within the business. That action could involve reallocating resources within the business to accommodate a need, or it could be identifying a failing campaign and knowing when to change course. These situations are where telling a story with your data is important.
Python is a popular programming language.
It is used for:
web development (server-side),
software development,
mathematics,
Data Analysis
Data Visualization
System scripting.
Python can be used for data analysis and visualization.
Data analysis is the process of analysing, interpreting, data to discover valuable insights that drive smarter and more effective business decisions.
Data analysis tools are used to extract useful information from business and other types of data, and help make the data analysis process easier.
Data visualisation is the graphical representation of information and data.
By using visual elements like charts, graphs and maps, data visualisation tools
provide an accessible way to see and understand trends, outliers and patterns in data.
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modelling, data visualization, machine learning, and much more.
Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data may be an Excel spreadsheet, or a collection of cloud-based and on-premises hybrid data warehouses. Power BI lets you easily connect to your data sources, visualize and discover what's important, and share that with anyone or everyone you want.
Power BI consists of several elements that all work together, starting with these three basics:
A Windows desktop application called Power BI Desktop .
An online SaaS (Software as a Service
) service called the Power BI service .
Power BI mobile apps for Windows, iOS, and Android devices.
These three elements—Power BI Desktop, the service, and the mobile apps—are designed to let you create, share, and consume business insights in the way that serves you and your role most effectively.
Beyond those three, Power BI also features two other elements:
Power BI Report Builder , for creating paginated reports to share in the Power BI service. Read more about paginated reports later in this article.
Power BI Report Server , an on-premises report server where you can publish your Power BI reports, after creating them in Power BI Desktop.
Tableau is a widely used business intelligence (BI) and analytics software trusted by companies like Amazon, Experian, and Unilever to explore, visualize, and securely share data in the form of Workbooks and Dashboards. With its user-friendly drag-and-drop functionality it can be used by everyone to quickly clean, analyze, and visualize your team’s data. You’ll learn how to navigate Tableau’s interface and connect and present data using easy-to-understand visualizations. By the end of this training, you’ll have the skills you need to confidently explore Tableau and build impactful data dashboards.
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