Ontwikkel je tot data analist

• Excel charts can be used for a myriad of data visualizations, including categorical data and continuous data, like time-series data. In this course, you'll learn how to bring data into Excel and build and customize various charts.
• You'll start by importing data from an existing workbook into a new spreadsheet. You'll then import data from CSV and JSON file formats and Microsoft Access database files. Next, you'll use the Power Query editor to perform various operations.
• Moving on, you'll create column and clustered column charts and perform various formatting operations on the clustered column chart, such as adding data labels, error bars, axis titles, and trendlines.
• Lastly, you'll create a simple line chart, formatting various aspects, such as the line, background, title, legend, axes, and position of charts relative to each other.

• Data visualizations in Excel reveal the insights uncovered by

• your data in easy-to-consume representations. You can identify
• categorical values, recognize how parts sum up to a whole, see
• percentages rather than absolute values, discretize continuous
• variables, and approximate the probability density function of
• variables. In this course, you'll build charts to uncover all of
• this information. You'll start by working with column and bar
• charts. You'll then create and differentiate between clustered and
• stacked column charts. You'll move on to formatting and customizing
• bar and column charts before working with 2D and 3D chart types and
• customizing them in various ways. Lastly, you'll work with
• histograms, examining how they work, what they're used for, and how
• to customize them to your needs.

• Line charts are possibly the most common type of visualization for time-series data, enabling you to see time trends at a glance. These can be augmented with trendlines, used to visualize time trends in data.
• Stacked area charts are a powerful type of visualization, combining information about trends over time with information about composition and parts of a whole.
• In this course, you'll learn how to create and customize all of the visualization types above.
• You'll begin by exploring the purpose of line charts before moving on to formatting and customizing them.
• You'll then practice using trendlines to evaluate different regression models on data in a line chart. You'll also customize and format these trendlines.
• Following this, you'll work with area charts and stacked area charts, examining, in detail, the several types of stacked area charts in Excel and customizing their appearance.

• Data visualization options in Excel are vast. You should choose your visualization type based on the data and what you want to show from it. For example, using High-Low-Close and Open-High-Low-Close charts (also called candlestick charts), you can summarize several stock performance aspects.
• Excel also lets you build radar charts - great for visualizing multivariate ordinal data, such as ratings or scores, to spot strengths or spikes.
• In this course, you'll not only learn how to build and customize the charts mentioned, but you'll also create treemaps to visualize hierarchical data and pie charts to display parts of a whole. You'll then generate pie-of-pie and bar-of-pie charts, both of which use a secondary visualization to complement a pie chart.
• Finally, you'll create donut charts to visualize composition using multiple concentric donut rings to represent points in time.

• Once you grasp how to work with the scope of standard Excel chart types, you can expand into more complex visualizations. For example, you can use box-and-whisker plots to convey a wealth of information about the statistical distribution of a variable and identify outliers in a data series.
• You can use sunburst charts to visualize hierarchical data with differing levels of detail, waterfall charts to show the cumulative effect of positive and negative values, and Gantt charts to illustrate progress toward a goal involving multiple parallel tasks.
• Additionally, you can avail of band charts to quickly eyeball the trend in a line chart, scatter plots to uncover the relationship between two variables, and waffle charts to visualize progress towards KPIs.
• In this course, you'll create all of these charts either via Excel's built-in tools or by building them manually using nifty workarounds.

Tableau is a data visualization tool suitable for a variety of purposes and situations. Knowing the basics of this tool will help you share necessary data with stakeholders and peers in a meaningful and engaging way. This course will introduce you to Tableau's basic features and cover the fundamental operations performed with this tool.

You'll start by loading data into Tableau using a variety of file types. You'll then apply transformations to your data and choose the most appropriate visualization for it. Next, you'll learn how to analyze data in multiple related tables as well as data categories. You'll then practice setting the aesthetics of charts and garnering detailed information through chart interactions. Lastly, you'll get hands-on with the Tableau data interpreter to clean a dataset.

Tableau not only has a variety of tools to visualize data, but it also allows you to redefine the fields in your data to convey rich meaning. In this course, you'll explore how multiple dimensions of the underlying data can be portrayed using shapes, colors, and labels.

You'll learn to build a wide variety of charts, such as pie charts, treemaps, and many more, to convey various information types. You'll also work with different techniques, such as hierarchical, combined, and calculated fields, to arrange the fields in the underlying dataset into hierarchies and groups, portraying relationships and facilitating data drill-downs for your users.

Lastly, you'll learn how you can generate sets from combined data, so this data can be analyzed with the context of the set, as well as outside it.

In addition to everything you'd expect from a data visualization tool, Tableau has extensive support for many advanced visual customization and user interaction features. This course gives you a taster of how these features work.

You'll start by using Tableau maps to render country-specific information and then further customize these maps, rendering their appearance and using map layers. You'll then assemble multiple charts together to collectively convey information using dashboards.

Next, you'll learn how to change the aesthetic appearance of Tableau dashboards and update them to include charts and other interactive objects and settings, such as navigation buttons, text labels, and download options. Lastly, you'll learn how to share a story publicly in the form of visualizations comprising charts and a dashboard using Tableau's publish feature.

• Power Platform, automates basic business processes with Power

• Automate, performing basic data analysis with Power BI, acting more
• effectively by creating simple Power Apps experiences, and creating
• powerful chatbots by using Power Virtual Agents. This course covers
• an overview of the platform, common data services, as well as a
• platform overview demo.

• Power Platform, automates basic business processes with Power

• Automate, performing basic data analysis with Power BI, acting more
• effectively by creating simple Power Apps experiences, and creating
• powerful chatbots by using Power Virtual Agents. This course
• covers: an introduction to power apps, building solutions, common
• data services, build an app and managing records.

Power Platform, automates basic business processes with Power Automate, performing basic data analysis with Power BI, acting more effectively by creating simple Power Apps experiences, and creating powerful chatbots by using Power Virtual Agents.

Power Platform, automates basic business processes with Power Automate, performing basic data analysis with Power BI, acting more effectively by creating simple Power Apps experiences, and creating powerful chatbots by using Power Virtual Agents.

• Data Science is a growing field, this initial course in Power BI
• describes what Power BI is as well as gets started with a project.
• Data Analysts are responsible for designing and building scalable
• data models, cleaning and transforming data, and enabling advanced
• analytic capabilities that provide meaningful business value
• through easy-to-comprehend data visualizations. The DA-100 Exam
• validates this expertise.

• Data Science is a growing field, this second course in the
• series digs into the Query Editor and Advanced Transformations.
• Data Analysts are responsible for designing and building scalable
• data models, cleaning and transforming data, and enabling advanced
• analytic capabilities that provide meaningful business value
• through easy-to-comprehend data visualizations. The DA-100 Exam
• validates this expertise.

• Data Science is a growing field, this course in the Power BI
• series digs into visualizing data as well as using Python in Power
• BI. Data Analysts are responsible for designing and building
• scalable data models, cleaning and transforming data, and enabling
• advanced analytic capabilities that provide meaningful business
• value through easy-to-comprehend data visualizations. The DA-100
• Exam validates this expertise.

• Data Science is a growing field, this course explores Data
• Analysis Expressions(DAX), the language developed to interact with
• data. Data Analysts are responsible for designing and building
• scalable data models, cleaning and transforming data, and enabling
• advanced analytic capabilities that provide meaningful business
• value through easy-to-comprehend data visualizations. The DA-100
• Exam validates this expertise.

• Data Science is a growing field, this final course in the series
• discusses how to tell an effective story as well as using alternate
• data sources. Data Analysts are responsible for designing and
• building scalable data models, cleaning and transforming data, and
• enabling advanced analytic capabilities that provide meaningful
• business value through easy-to-comprehend data visualizations. The
• DA-100 Exam validates this expertise.

• This course discusses Row-Level Security Data Analysts are
• responsible for designing and building scalable data models,
• cleaning and transforming data, and enabling advanced analytic
• capabilities that provide meaningful business value through
• easy-to-comprehend data visualizations. The DA-100 Exam validates
• this expertise.

• start the course
• identify a database and how it relates to SQL
• describe the relational concepts of SQL, various database
• types, and when and why they are used
• use SQL to describe the differences between SQL and other
• languages
• describe in high level, structured query language and its
• use
• use SQL to create a simple statement
• describe data types and their relation to SQL
• use SQL to create a simple database example
• describe tables and how to create them in SQL
• use tables to further dive into the complexities of SQL
• use the INSERT statement in SQL
• use SQL to create more complex tables
• use SQL to alter an existing table
• use SQL to delete an existing table
• use SELECT to take a look at a table
• use the NULL statement in SQL
• use the DEFAULT statement in SQL
• use the SELECT statement in SQL queries
• use SELECT to retrieve data from more than one table in
• SQL
• use SQL to create several data types
• use DELETE to remove data from a table in SQL
• use UPDATE to change data in SQL
• use ORDER BY with results to sort data in SQL
• use WHERE to filter data and retrieve requested results in
• SQL
• use all data management components in an example in SQL
• describe SQL and the query while demonstrating how to create a
• table and manipulate it

• start the course
• use PRIMARY KEY in SQL
• use both CREATE TABLE and add PRIMARY KEY constraint in SQL
• use both ALTER TABLE AND PRIMARY KEY constraint in SQL
• describe how to rename and alter a table in SQL
• use SQL to ALTER and CHANGE data
• describe DROP COLUMN and its use in SQL
• describe a use case example of executing altering queries in SQL
• describe the various math operators and functions
• use SQL rounding numbers process
• use SQL MIN and MAX processes
• use SQL AVG and GROUP BY
• use LIMIT and SELECT DISTINCT
• use SQL arithmetic and grouping to create queries
• describe the SUBSTRING() functioning in SQL
• using the LENGTH function in SQL
• use the REVERSE() function in SQL
• use the TRIM() function in SQL
• use date functions in SQL
• use SOUNDEX() and DIFFERENCE() functions in SQL
• use SQL string functions to create a query
• manage data using SQL functions

• start the course
• define multiple tables and describe their use in SQL
• use SQL for the process of connecting tables
• use FOREIGN KEY with CREATE in SQL architecture
• describe relationships between tables and the different patterns
• use composite keys and their relative values
• describe the functional dependencies and their use
• use functional dependencies in a example
• use inner joins in SQL
• use equijoins and non-equijoins and describe their differences in SQL architecture
• use Outer Joins in SQL
• use multiple joins and multiple conditions in SQL
• use the UNION operator and describe how it combines results
• describe a subquery and its use in SQL
• use query as a SELECT column in SQL
• use correlated and noncorrelated queries in SQL
• use a query with a natural join
• use IN, NOT IN commands in SQL
• use correlated query with EXISTS and NOT EXISTS operators
• use HAVING clause in SQL with queries
• use queries with the UPDATE statement in SQL
• use queries with the INSERT statement in SQL
• use queries with the DELETE FROM statement in SQL
• use SQL to create a more complex query
• use SQL more efficiently with operational tips
• describe the multiple layers of tables and their management in various SQL queries

• start the course
• describe views and their relation to the SQL architecture
• use SQL to create a simple view
• use the various views in SQL architecture
• use SQL to update a view
• use SQL to drop views
• describe the various transactions and their use in SQL
• describe the various ACID transactions and their use
• use Transactions in SQL
• use ROLLBACK statement in SQL
• use the COMMIT statement in SQL
• use ROLLBACK and COMMIT in an SQL query
• use SQL to define Transact-SQL and the relation with transaction types
• use transaction logs and define their purpose in SQL
• describe locks and their use in SQL
• use SQL to introduce the various levels of locking and their use
• use SQL to describe the various lock modes
• use SQL to identify the levels of locking
• use SQL to create an implementation using locks
• describe the various security concepts in SQL
• use SQL to describe the philosophy behind User IDs and their use
• use SQL to describe how to create a user and how to manage them
• use SQL to describe the use of group IDs and roles in
• use SQL to define the use of privileges and restrictions
• use SQL security processes to create a user and a group with privileges
• describe views, transactions, and the security model and the relation to SQL

• start the course
• describe the features of the Python programming language and how and where it is used
• describe the philosophy of Python
• recognize reasons to choose one version of Python over the other
• install Python 3 on Windows
• install Python 3 on Mac OS X and Linux
• evaluate the major IDEs available for Python
• use whitespace to lay out a Python program into functional code blocks
• recognize the Python REPL – read, evaluate, print loop
• create and execute a "Hello World" application with Python
• get and manipulate user input from the command line with Python
• create a module and import a module in Python
• use the int data type in Python and recognize its characteristics
• use the float data type in Python and recognize its characteristics
• perform basic math functions, such as addition, subtraction, multiplication and division, and use the Math module
• use the bool data type in Python and recognize its characteristics
• describe sequence types and use the str type in Python
• use the bytes type in Python
• use the bytearray type in Python
• use the list type in Python
• use the tuple type in Python
• use slicing on sequence types in Python
• use the range function and work with range objects in Python
• use the set type in Python and describe its characteristics
• use the dict type in Python and describe its properties
• construct a while loop in Python
• construct a for loop in Python
• use the if statement in Python to control program flow
• write a Python program to reverse user input

• start the course
• create and import a module at the Python REPL
• define a function in Python
• describe the difference in operation between Python scripts, programs and modules
• run a module as a script using the __name__ == __main__ syntax in Python
• create a main function that takes command line arguments in Python
• describe the relationship between classes and types in Python
• create a class definition and describe the structure in Python
• write a class initializer method in Python
• write and use class instance methods in Python
• write and use static methods in Python
• use inheritance and describe the semantics in Python
• describe class properties in Python
• describe how inheritance affects properties in Python
• write a class that implements operator overloading in Python
• write docstrings in Python
• write comments in Python
• describe best practice for documenting Python code as set out in PEP 8
• read text files in Python
• write data in Python
• write large files in Python
• read binary data in Python
• write binary data in Python
• write a Python class to represent a vector

• start the course
• create a list comprehension in Python
• create a nested comprehension in Python
• use the zip() function in a generator in Python
• create a set comprehension in Python
• create a dictionary comprehension in Python
• describe the function of iter(), next() and StopIteration() in Python iteration
• use the map() function in an iteration in Python
• use the filter() function in an iteration in Python
• use functools.reduce() to iterate over an iterable
• implement a custom iterable class in Python
• implement an iterable using consecutive integer indexing in Python
• implement an iterable using the extended iter() function
• create a simple generator in Python
• create a lazy generator in Python and understand its characteristics
• create a recursive generator in Python
• write a basic exception handler in Python to catch all exceptions
• write an exception handler in Python to catch a specific error, and recognize the reason why catching all errors is bad practice
• describe the inheritance hierarchy of exceptions in Python and how to catch multiple exception types using a base type
• raise an exception using a payload and retrieve a payload when handling an error
• create a custom exception class in Python
• access and manipulate traceback objects for an exception in Python
• use assertions in a Python program
• use implicit and explicit chaining of exceptions in Python
• create an iterable data type that handles exceptions in Python

• start the course
• describe the key features of the Django framework
• install and configure the Django framework
• create a Django project
• configure the Django web server
• create a sample Django app
• incorporate views and templates in an app
• use Django to include data in a Python web application
• utilize forms in a Python web application
• describe the key features of the TurboGears framework
• install and configure the TurboGears framework
• incorporate TurboGears templates into a Python web app
• incorporate TurboGears views into a Python web app
• create and use a controller in a Python web app
• describe rendering and how it is used in TurboGears
• use TurboGears to include data in a Python web application
• use RESTful URLs in TurboGears
• describe the key features of Flask
• create a basic Flask application
• incorporate a template into a Flask app
• work with web forms in a Flask project
• connect to and retrieve data using a Flask app
• use Django to create a view for a Python web application

Python is a high-level programming language that has code readability and simplicity as its primary design goals. Coupled with a few key APIs, it also becomes a very powerful data analysis tool. This course will cover basic data science fundamentals and apply them to Python.

• The free and robust statistical package R has been decades in the making and is worth learning for serious statistical operations, such as conducting new medical data analysis.
• This course teaches you everything you need to know to get started with R, from installing R to running R from the command line.
• You'll grasp how to invoke basic functions and view the documentation on those. You'll create variables in R and explore various reserved words and the = and <- operators.
• You'll then perform basic arithmetic operations on variables, invoke built-in functions, and work with various atomic data types, such as character, integer, double, logical, complex, real, and raw.
• By the end of this course, you'll have the skills you need to get working with R.

• Vectors are the easiest type of data structures in R. However, to use them successfully, it's important to appreciate their restrictions, recognize the types available, and identify their members - or components as they're officially called in R.
• This course shows you how to create and generate vectors using the c() and vector() functions, respectively.
• You'll perform vectorized operations on elements in vectors. Practice filtering and slicing vectors. And use the which(), any(), and all() functions on vectors.
• Furthermore, you'll perform naming and indexing operations on vectors and work with different length vectors using vector recycling.
• On completing this course, you'll have the knowledge and know-how to utilize vectors for their intended purpose.

• Vectors are a great basic data structure in R, but they have important limitations on the dimensions and types of data they contain. Matrices, arrays, and lists are powerful R structures that mitigate these limitations. This course will help you distinguish each of these three elements' purpose and show you how to use them.
• You'll start by using matrices to store two-dimensional data. You'll then differentiate between row-major and column-major matrices.
• You'll learn how to use arrays and how you can easily create three-dimensional arrays as you can two-dimensional arrays.
• You'll then move on to the use of lists and how they differ from vectors. After taking this course, you'll be able to identify when and how to use a matrix, a list, and an array.

• Data frames are an R abstraction for tabular data similar to that contained in spreadsheet files or database tables. Data frames can work directly with files in the CSV, JSON, and Excel format, all common formats used to store data.
• This course outlines the characteristics of data frames in the R programming language and demonstrates how to use them.
• You'll learn to create basic R data frames from multiple vectors. You'll use factors - similar to enums or enumerated types in other programming languages and great for categorical variables.
• You'll also learn how to perform various string manipulation operations, such as splitting and joining strings and changing case. You'll then practice the important topic of printing precisely formatted strings with placeholders for variable values.
• When you're done, you'll be able to use data frames, factors, and strings professionally in your R programming projects.

Answers to key questions : How to overcome nerves? Is it good to use notes? How much do should you memorize? What do you if you forget half way through?