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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:
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web development (server-side),
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software development,
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mathematics,
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Data Analysis
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Data Visualization
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System scripting.
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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:
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A Windows desktop application called Power BI Desktop.
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An online SaaS (Software as a Service) service called the Power BI service.
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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:
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Power BI Report Builder, for creating paginated reports to share in the Power BI service. Read more about paginated reports later in this article.
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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.
Data Analysis with Python
Data Analysis with Power BI
Data Analysis with Excel
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21What is Power BI
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22What is Power BI Desktop
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23Installing Power BI Desktop
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24Power BI Desktop tour
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25Power BI Overview: Part 1
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26Power BI Overview: Part 2
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27Power BI Overview: Part 3
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28Components of Power BI
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29Building blocks of Power BI
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30Exploring Power BI Desktop Interface
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31Exploring Power BI Service
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32Power BI Apps
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33Please Note
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34Connecting to web data
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35Clean and transform data : Part 1
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36Clean and transform data : Part 2
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37Combining Data Sources
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38Creating Visualization : Part 1
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39Creating Visualization : Part 2
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40Publishing Reports to Power BI Service
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41Importing and transforming data from Access db file
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42Changing locale
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43Connecting to MS Access DB File
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44Power query editor and queries
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45Creating and managing query groups
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46Renaming Queries
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47Splitting Columns
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48Changing Data Types
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49Removing and reordering columns
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50Duplicating and adding columns
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51Creating conditional columns
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52Connecting to files in folder
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53Appending queries
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54Merge queries
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55Query dependency view
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56Transform less structured data: Part
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57Transform less structured data: Part 2
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58Creating tables
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59Query Parameters
Data Analysis with Tableau
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60Office 365 setup ( Optional)
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61Activating office 365 ( Optional)
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62Logging into office 365 (Optional)
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63What is Power Pivot
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64Office versions of power pivot
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65Enable Power Pivot in excel
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66What is Power Query
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67Connecting to a data source
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68Preparing query
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69Cleansing data
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70Enhancing query
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71Creating a data model
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72Building data relationships
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73Create lookups with DAX
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74Analyse Data with Pivot Tables
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75Analyse data with Pivot Charts
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76Refresh Source Data
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77Update Queries
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78Create new reports