Data Analysis & Visualization: Python | Excel | BI | Tableau
- Description
- Curriculum
- FAQ
- Reviews
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.
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1IntroductionVideo lesson
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2What is PythonVideo lesson
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3What is Jupyter NotebookVideo lesson
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4Installing Jupyter Notebook ServerVideo lesson
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5Running Jupyter Notebook ServerVideo lesson
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6Common Jupyter Notebook CommadsVideo lesson
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7Jupyter Notebook ComponentsVideo lesson
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8Jupyter Notebook DashboardVideo lesson
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9Jupyter Notebook InterfaceVideo lesson
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10Creating a new Jupyter NotebookVideo lesson
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11Kaggle DatasetsVideo lesson
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12Tabular dataVideo lesson
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13Exploring Pandas DataFrameVideo lesson
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14Analysing and manipulating pandas dataframeVideo lesson
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15What is data cleaningVideo lesson
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16Basic data cleaningVideo lesson
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17Data VisualizationVideo lesson
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18Visualizing qualitative dataVideo lesson
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19Visualizing quantitative dataVideo lesson
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20What is Power BIVideo lesson
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21What is Power BI DesktopVideo lesson
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22Installing Power BI DesktopVideo lesson
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23Power BI Desktop tourVideo lesson
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24Power BI Overview: Part 1Video lesson
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25Power BI Overview: Part 2Video lesson
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26Power BI Overview: Part 3Video lesson
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27Components of Power BIVideo lesson
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28Building blocks of Power BIVideo lesson
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29Exploring Power BI Desktop InterfaceVideo lesson
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30Exploring Power BI ServiceVideo lesson
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31Power BI AppsVideo lesson
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32Connecting to web dataVideo lesson
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33Clean and transform data : Part 1Video lesson
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34Clean and transform data : Part 2Video lesson
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35Combining Data SourcesVideo lesson
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36Creating Visualization : Part 1Video lesson
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37Creating Visualization : Part 2Video lesson
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38Publishing Reports to Power BI ServiceVideo lesson
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39Importing and transforming data from Access db fileVideo lesson
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40Changing localeVideo lesson
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41Connecting to MS Access DB FileVideo lesson
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42Power query editor and queriesVideo lesson
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43Creating and managing query groupsVideo lesson
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44Renaming QueriesVideo lesson
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45Splitting ColumnsVideo lesson
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46Changing Data TypesVideo lesson
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47Removing and reordering columnsVideo lesson
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48Duplicating and adding columnsVideo lesson
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49Creating conditional columnsVideo lesson
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50Connecting to files in folderVideo lesson
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51Appending queriesVideo lesson
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52Merge queriesVideo lesson
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53Query dependency viewVideo lesson
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54Transform less structured data: Part 1Video lesson
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55Transform less structured data: Part 2Video lesson
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56Creating tablesVideo lesson
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57Query ParametersVideo lesson
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58Office 365 setup ( Optional)Video lesson
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59Activating office 365 ( Optional)Video lesson
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60Logging into office 365 (Optional)Video lesson
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61What is Power PivotVideo lesson
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62Office versions of power pivotVideo lesson
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63Enable Power Pivot in excelVideo lesson
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64What is Power QueryVideo lesson
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65Connecting to a data sourceVideo lesson
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66Preparing queryVideo lesson
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67Cleansing dataVideo lesson
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68Enhancing queryVideo lesson
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69Creating a data modelVideo lesson
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70Building data relationshipsVideo lesson
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71Create lookups with DAXVideo lesson
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72Analyse Data with Pivot TablesVideo lesson
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73Analyse data with Pivot ChartsVideo lesson
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74Refresh Source DataVideo lesson
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75Update QueriesVideo lesson
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76Create new reportsVideo lesson
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