Requirements
No prior experience is necessary. We will start from the very beginning.
You will need to install Anaconda. We will guide you through the installation process step by step.

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Description
The Issue

Many data analyst, data science, and coding courses overlook a crucial practical step. They fail to teach you how to work with raw data, how to clean and preprocess it. This creates a significant gap between the skills required for the job and the skills acquired during training. The truth is, real-world data is often messy, so it is essential to know how to overcome this obstacle in order to become an independent data professional.

Online bootcamps and even live classes tend to neglect this aspect and only focus on working with "clean" data. However, this approach does not benefit you in the long run. In reality, it will hinder your job applications and performance on the job.

The Solution

Our objective is to provide you with comprehensive preparation. This course will transform you into a data analyst who is ready for the job market. To achieve this, we will extensively cover the following fundamental topics:

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- Theoretical knowledge about the field of data analytics
- Basic Python programming
- Advanced Python programming
- NumPy library
- Pandas library
- Working with text files
- Data collection
- Data cleaning
- Data preprocessing
- Data visualization
- Final practical example

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Each of these subjects builds upon the previous ones, making our curriculum highly valuable. We have structured the course in a logical order, ensuring that you won't feel lost along the way. We have included all the necessary steps in video format, leaving no gaps. In other words, we won't teach you how to analyze data before you learn

 

how to gather and clean it.

Therefore, to prepare you for an entry-level job that can lead to a data science position, such as a data analyst, we have developed The Data Analyst Course.

This training program is unique because it focuses on teaching the fundamental skills required for the job. It addresses an often overlooked aspect that is of vital importance.

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Moreover, we focus on delivering topics that flow smoothly and complement each other. This course offers comprehensive preparation for those who want to become  data analysts at a fraction of the cost  (not to mention the time savings) of traditional programs. We believe  this resource will significantly increase your chances of getting hired by preparing you for practical tasks and concepts that often come up in interviews.

Topics Covered

1. Theory in the Data Analytics Field

2. Basic Python

3. Advanced Python

4. NumPy 5. Pandas

6. Working with Text Files

7. Data collection

8. Data cleansing

9. Data preprocessing

10. Data visualization

11. Final practical example  

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1. Theory of the Data Analytics Field Here we  focus on the big picture. But don't imagine long, boring pages of terms that you have to look up in the dictionary every minute. Instead,  we want to define who a data analyst is, what they do, and how they add value to a company.

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Why do you need to learn this?

You need a general understanding to understand how each part of the course fits  with the rest of the content. As the saying goes, if you know where you want to go, you will eventually get there. And since data analyst and other data-related jobs are relatively new and constantly evolving, we want you to have a good understanding of the role of a data analyst in particular. Then, in the next chapter, we'll cover the actual tools you need to become a data analyst.

2. Basic Python

This course is about Python. So let's start with the  basics. Don't worry if you don't have any programming experience.

Why learn it?

To make the most of the data-rich world we live in, you need to learn a programming language. Without such skills, you will always be dependent on the abilities of others to extract and manipulate data, and you want to be independent when it comes to analysis, right? Moreover, you don't necessarily need to learn several programming languages ​​at the same time; knowing one well is enough. We naturally chose Python, which has established itself as the number one language for data analysis and data science (thanks to its rich libraries and versatility).

3. Advanced Python

Introduces advanced  topics in Python, including working with text data and using tools such as list comprehensions and anonymous functions. Why learn it?  These lessons will turn you into a proficient Python user who can work independently, using Python's core strengths to your advantage, so they're not just about the topics, but also  the depth to explore the most important Python tools.

Why learn it?  

These lessons will turn you into a proficient Python user who can work independently. They will allow you to use Python's core strengths to your advantage. So they're not just about the topics, but also  the depth to explore the most important Python tools.

4. NumPy

NumPy is Python's base package for scientific computing. It has established itself as the  tool of choice when you need to calculate mathematical and statistical operations.

Why learn it?

A large part of a data analyst's work is the preprocessing of data sets. Undoubtedly, this involves a large amount of mathematical and statistical techniques for which NumPy is known. Additionally, the package introduces multidimensional array structures and provides a wealth of built-in functions and methods that can be used when manipulating them. In other words, NumPy is a computationally stable, cutting-edge Python tool that gives you flexibility and allows you to take your analysis to the next level.

5. Pandas

The Pandas library is one of the most popular Python tools that makes it easy to manipulate and analyze data. It is extremely valuable because it allows you  to work with any kind of information, including text as well as numerical tables and time series data.

Why do you need to learn it?

Pandas is another key tool that analysts have to clean and preprocess the data they work with. Its data manipulation capabilities are unmatched in Python, as it is so diverse and rich in terms of methods and functions. The  ability to have both NumPy and Pandas working together is very powerful, as the two libraries complement each other. Both should be able to work together to produce a complete and coherent analysis compared to each other individually.

6. Working with Text Files

Sharing information using text files is  how we actually share information today. In this part of the course, we will teach you the basics needed to import or save data using the Python, Pandas, and NumPy tools you learned earlier.

Why do you need to learn this?

Many courses only give you  a dataset to practice your analytical and programming skills. However, we don't want to close our eyes to the reality that converting  raw datasets from  external files into a processable Python format can be a big challenge.

7. Data Collection

In the real world, data is not always  readily available. In this part of the course, you will learn how to get data from an API.

Why do you need to learn this?

You need to know how to source  data, right? To be a well-rounded analyst, you must be able to gather data from external sources. This is rarely a one-click process. This section aims to give you  all the  tools you need to do this yourself.

8. Data Cleansing

The next logical step is to clean the data. This is where you actually apply the Pandas knowledge you acquired earlier. Every lesson in  the course contains a real-world perspective. Why do you need to learn this?

A large part of a data analyst's job in the real world is cleaning data and preparing it for real analysis. You can’t expect to work with an error-free data source, can you? So it’s up to you to get past this stage and clean up your data.

9. Data Preprocessing

Even if your dataset is in a clean and  understandable format, it’s not yet ready to be processed for visualization or analysis. There’s a crucial step in between, and that’s data preprocessing.  

Why do you need to learn it?

Data preprocessing is how data analysts show how good  their work is. This step requires the ability to choose the right statistical tools to improve the quality of your dataset and the knowledge to implement it using advanced Pandas and NumPy techniques. Only after completing this step can we say that our dataset is preprocessed and ready for the next part: data visualization.

10. Data Visualization

Data visualization is the face of data. Many people look at  data and don't understand anything. The reason  is that they can't create a proper visualization. Even worse, they can create beautiful graphics but can't interpret them accurately.

Why should you learn it?

In this part of the course, you will learn how to use  data to generate meaningful insights. After all, data charts convey the most information in the shortest amount of time. And nothing speaks more eloquently than a well-designed, meaningful data visualization.

11. Practical Examples

The course includes numerous exercises and practical cases. Finally, to show you how everything you've learned so far fits together nicely, we've included a comprehensive hands-on example, where you can see how far you've come on the path to becoming a data analyst and starting your data career.

What you'll get

$1,250 worth of programs

Active Q&A support

Everything you need to know to become a data analyst

A community of aspiring data analysts

Certificate of completion

Access to frequent future updates Practice

Get ready to become a data analyst from scratch

Why wait?

Every day is a lost opportunity.

Click the "Get Quote" button and join our Data Analyst program now. Who is this course  for: If you want to be a data analyst or data scientist then you must take this course. This course is perfect for you if you want a great career This is also a great course for beginners as you start with the basics and gradually build up your skills.