Business Intelligence Analyst Track
As a business intelligence analyst, you will be using inhouse data to provide market and business insights. These insights allow companies to improve and increase profitability.There is a difference between a business analyst and a business intelligence analyst. While, a business analyst can work with data and make data-driven decisions, a business intelligence analyst is required to perform much more technical analyses on larger datasets, using a degree of programming. The role combines the data and business worlds and provides you with skills that are easy to transfer to other business or data science positions
What’s included and why
Intro to data and data science
Working with data is an essential part of maintaining a healthy business. This course will introduce you to the field of data science and help you understand the various processes and distinguish between terms such as: ‘traditional data’, ‘big data’, ‘business intelligence’, ‘business analytics’, ‘data analytics’, ‘data science’, and ‘machine learning’.
More info goes here
Section 1: Introduction
We will start with an introductory lecture about the 365 Data Science program. We will discuss the best way to approach our trainings and how to take our courses in a way that will position you well for a data scientist career.
Section 2: The different data science fields
For a novice, the data science field can be rather confusing. It takes a while to make sense of all the buzz words and different areas of data science. Do not worry, as we will make this process easier and much faster for you. In some of our first lessons, you will learn how to distinguish between Business analytics, Data analytics, Business Intelligence, Machine Learning, and Artificial Intelligence. With this knowledge we point out the place of data science as of today. The specially designed infographic we will discuss in the lessons makes everything clearer.
Section 3: The relationship between different data science fields
In this chapter, you will learn how data science fields relate to each other and which ones leverage on:
- traditional and big data
- business intelligence
- traditional data science methods and machine learning
Section 4: What is the purpose of each data science field
It is one thing to learn which are the various data science disciplines, but a whole different story to be able to tell what each discipline is used for in practice. This is really valuable for you as it will allow you to gain an idea of the practical application of the different methods you will learn later on in our program.
Section 5: Common data science techniques
There are different ways to approach Traditional data, Big data, Business Intelligence, Traditional data science methods, and Machine learning. In this part of the course, we will introduce you to some of the most common techniques to do that, and we will provide several practical examples that will make things easier and more relatable.
Section 6: Common data science tools
Before we dive in to studying the different types of tools used in data science, we will provide a quick overview for you, so you can have a good idea of why we are studying different tools and how they relate with each other. This will greatly facilitate your learning process as you will already know what to expect and for what tasks you will need a tool exactly.
Section 7: Data science career paths
As with most professions, there are different career paths you can embark upon. In this chapter, we will discuss several job positions related to the fields of data and data science.
Section 8: Dispelling common misconceptions
Finally, we will conclude our Intro to Data and Data Science training with a few lessons dispelling the most common misconceptions about the data science field.
Statistics is the driving force in any quantitative career. It is the fundamental skill BI analysts need to be able to understand and design statistical tests and analyses performed by modern software packages and programming languages. We will start from the very basics and will gradually build up your skills allowing you to understand more complex analyses carried out later.
More info goes here
Section 1: Introduction
In this introductory part of the course, we will discuss why you need to learn Statistics, and which are the key skills you will acquire by taking the course.
Section 2: Fundamentals of descriptive statistics
Understand the basic features of data. There are different types of data and levels of measurement. After you complete this section, you will be able to distinguish between them and will know the difference between categorical and numerical values. All of this will help you when calculating the measures of central tendency (mean, median, and mode), and dispersion indicators such as variance, standard deviation, as well as measures of relationship between variables like covariance, and correlation. To reinforce what you have learned, we will wrap up this section with an easy to understand practical example.
Section 3: Fundamentals of inferential statistics
One of the core topics you will find in every Statistics text book is about distributions. In this part of the course, you will learn what a distribution is and what characterizes the normal distribution. We will introduce you to the central limit theorem and to the concept of standard error. Pretty soon you will be able to calculate confidence intervals with known population and variance. And once we introduce the Student T distribution, you learn how to work with smaller samples, as well as differences between two means (with dependent and independent samples). All of these tools will be fundamental later on when we start applying each of these concepts to large datasets and use coding languages like Python and R. To reinforce what you have learned, we will wrap up this section with an easy to understand practical example once again.
Section 4: Hypothesis testing
Confirming and rejecting hypothesis with a reasonable degree of certainty is a practical and easy to apply method when dealing with uncertainty. In this section, you will learn how to perform hypothesis testing and what is the difference between a null and alternative hypothesis, as well as rejection and significance level, type I and type II errors. The lessons will teach you how to test for the mean when the population variance is known and unknown, as well as how to test for the mean when you are dealing with dependent and independent samples. We should not forget to mention that this is the part of the course when you will become familiar with the p-value, a key measure when dealing with advanced models. Similar to previous sections, we will conclude with a practical example, to make use of our new knowledge.
Section 5: Regression analysis
Regression analysis is arguably the most common method of prediction. It is used to describe the causal relationship between variables. We will start by introducing the linear regression model and will explain the difference between correlation and regression. Then we will be ready to introduce concepts like regression tables, R-squared, adjusted R-squared, ordinary least squares, and more. When we use multiple independent variables, we use a multiple linear regression. Once you have learned how to carry out simple and multiple linear regressions, we will introduce the five regression assumptions (linearity, no endogeneity, normality and homoscedasticity, no autocorrelation, and no multicollinearity). Naturally, we will put our newly acquired regression knowledge to use in a practical example which concludes the course.
Microsoft Excel is the #1 productivity software in the world. A huge amount of data comes in a spreadsheet format, so an analyst needs Excel in their arsenal. This course will teach you all the Excel skills you need to perform multi-layered calculations, create charts, manipulate data, lookup functions, and more!
More info goes here
Section 1: Course Introduction
In this introductory part of the course, we will discuss why you need to learn Excel, and which key skills you will acquire by taking the course.
Section 2: A quick introduction to the basics of Excel
This section is fundamental for those of you who have never used Excel. We will start from the very basics: introducing the Excel ribbon, learning how to insert (and delete) rows and columns, how to perform data entry tasks, and how to format worksheets professionally. In addition, you will create your first formulas and functions, and cut, copy, and paste values for the first time.
Section 3: Excel useful tips & tools
Once you are familiar with the basic operations in Excel, it will be time to learn Excel best practices and learn how to navigate spreadsheets professionally. In no time you will know how to apply fast scrolling, use keyboard shortcuts, format sheets professionally, fix cell references, use named ranges, apply custom cell formats, and much more.
Section 4: Excel functions
Excel is one of the most popular productivity tools the business world has ever seen. The main reason for this is Excel functions. It is time for you to learn how to use Excel functions like a true professional. We will start with some easier examples (SUM, COUNT, AVERAGE, IF, MAX, MIN, VLOOKUP, HLOOKUP), and gradually introduce more advanced (and more powerful) functions such as SUMIF, SUMIFS, COUNTIF, COUNTIFS, INDEX, MATCH, INDEX & MATCH, etc.
Section 5: Excel charts
One of the strongest features of Microsoft Excel, besides multi-layered calculations, is that it allows you to visualize data. Here you will learn how to insert and format different types of charts that will help you make sense of numbers and figure out their trend.
Section 6: Practical exercise – Build a P&L from scratch
It is one thing to learn how to work with Excel’s most important tools, but it is even better to apply these techniques in a practical exercise. This is what we will do here. The “Build a P&L from scratch” exercise allows you to see how everything you have learned so far can be put into practice.
Tableau is now one of the most popular business intelligence tools in the world. It allows non-technical users to visualize data and work with it immediately. This course shows you how you can use Tableau to create state-of-the-art visualizations and powerful dashboards, precisely what corporate executives need when making decisions.
More info goes here
Section 1: Why Tableau? Introduction to Tableau and how to get started
Tableau is an indispensable tool in the arsenal of most corporate business intelligence analysts, data analysts, and data scientists. Many people are uncertain about the difference between Tableau and spreadsheet tools like Excel. And that’s a reasonable doubt. In this part of the course, we will explain when and why you need Tableau, as well as the difference between Tableau and spreadsheet tools like Excel. We will also teach you how to install Tableau Public (Tableau’s free version).
Section 2: Tableau’s interface and connecting data
Once we have installed Tableau Public, we will be ready to go through Tableau’s interface and describe its different parts succinctly. You will also learn how to connect Tableau to Excel, csv, and other types of files or environments containing data.
Section 3: Basic operations in Tableau: creating a chart, creating a table
It is time to create our first Tableau charts and tables. You will learn how to create basic charts and adjust parts of their appearance.
Section 4: Additional Tableau functionalities
Some of the additional Tableau functionalities we will cover here are: creating custom fields, adding calculations to a table, adding totals and subtotals, and adding a filter.
Section 5: Practical exercise (part 1): Connecting data
The final part of our Tableau training is a complete practical example. The exercise is divided in two parts. In the first part, we will test several ways to connect our data. You already studied joins in SQL. Here, you will see how to apply joins in a Tableau context. Moreover, we will teach you about data blending, and discuss best practices related to joining and blending data.
Section 6: Practical exercise (part 2): Creating a dashboard
Once we have connected our data and have verified it is ready, we will be ready to create the three charts intended at the beginning of the exercise. Each of these charts analyzes a different aspect of a real-life dataset. We will group the charts in a dashboard, and add a filter, which isu applied to all three charts at the same time.
SQL is one of the fundamental programming languages you need to learn to work with databases. When you are a business intelligence analyst in a company and you need data to perform your analysis, you usually have two options: extract it on your own or contact the IT team. Of course, the first one is an extremely valuable skill to have. In this course, we will teach you everything you need to know in terms of database management and creating SQL queries.
More info goes here
Section 1: Introduction to databases, SQL, and MySQL
Whether you are working in business intelligence (BI), data science, database administration, or back-end development, you will have to retrieve information from a server storing large amounts of data. To achieve this, you need SQL. The relational database management system we chose for this course is MySQL. We did that because MySQL is open-source, reliable, and mature. In one of the videos of this section, we will provide you with step-by-step guidance when you install MySQL Server and MySQL Workbench. The introductory part of this course pays significant attention to database theory. You will learn the meaning of terms like database, data table, data entity, record, field, relation, and more.
Section 2: First steps in SQL
It is time to create your first database and make your first steps in SQL. In this section, we will introduce you to string, fixed- and floating-point, and other useful data types. You will learn how to create a database table and how to use such a table. Not only that, but we will also introduce the different types of constraints that can be assigned to tables (primary key, foreign key, unique key, default, not null, and other types of constraints)
Section 3: SQL best practices
There are many ways you can write your SQL code, but there are only a few that are considered professional. In this part of the course, we will teach you how to write professional code and how to adhere to professional best practices. To reinforce what you have learned, we will wrap up this section with an easy to understand practical example.
Section 4: Loading the ‘employees’ database
One of the best features of our SQL training is that it uses a real-life database – the “Employees” database. We will use it to manipulate data in MySQL in all lessons. In this chapter, you will download the SQL file and will run it in Workbench.
Section 5: Data manipulation in SQL: SELECT, INSERT, UPDATE, DELETE
Are you ready to learn some of the most frequently used tools in SQL? These are the SELECT, INSERT, UPDATE, and DELETE statements. We use these statements to extract, insert, update, and delete data from a database.
Section 6: MySQL Aggregate functions
Aggregate functions come in handy when we want to perform some arithmetic operations with the data in our database. The most commonly used aggregate functions in SQL are COUNT(), SUM(), MIN(), MAX(), and AVG().
Section 7: SQL Joins, subqueries, self joins, and views
Joins are one of the most powerful and frequently used tools in SQL. This is a tool you will need when combining the information from two or more tables. After completing this section, you will be able to use inner, left, right, and self joins. You will also learn how to write subqueries and views. The section includes a number of useful tips and tricks and aims to take your SQL skills to the next level.
Section 8: Stored routines
Stored routines are a set of SQL statements that have been pre-written and stored on a server allowing users to re-run them at a later stage. You will learn how to create your own stored procedures and functions.
Section 9: Advanced SQL Topics
In the last part of the training, you will learn advanced SQL topics like local variables, session variables, global variances, MySQL triggers, and MySQL indexes.
SQL + Tableau
It is next to impossible to analyse data in SQL. This course will give you methods of using SQL and Tableau simultaneously, helping you find hidden value in your data faster than using them separately.
More info goes here
Section 1: Introduction
In the introductory part of the course, we will talk about the lessons you will see next and discuss why a data scientist would want to be able to connect tools like SQL and Tableau.
Section 2: How to connect SQL and Tableau
The integration between the two tools is not that difficult. Especially, if you work with Tableau Desktop. In this section, we will show you how to do that, and we will provide you with a workaround in case you use Tableau Public.
Section 3: Practical exercise – Part 1
We will continue to use the ‘employees’ database we worked with in a large part of our SQL training. By connecting Tableau and SQL you will be able to visualize several trends related to the company’s gender gap situation
Section 4: Practical exercise – Part 2
Here we will continue to explore the ‘employees’ database and will run several sophisticated SQL queries to answer the remaining questions in our task. The topic under investigation continues to be ‘gender gap’ policies. We will gather our data in a well-organized dashboard and will show you how to add an interactive filter which makes analysis much quicker and intuitive