The Data Analyst Course: Complete Data Analyst Bootcamp 2022 (2023)

Complete Data Analyst Training: Python, NumPy, Pandas, Data Collection, Preprocessing, Data Types, Data Visualization

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Jul 6, 2022-17:42Updated:Jun 29, 2021-06:28

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The Data Analyst Course: Complete Data Analyst Bootcamp 2022 (2)

The Data Analyst Course: Complete Data Analyst Bootcamp 2022 udemy course free download

Complete Data Analyst Training: Python, NumPy, Pandas, Data Collection, Preprocessing, Data Types, Data Visualization

What you'll learn:

  • The course provides the complete preparation you need to become a data analyst

  • Fill up your resume with in-demand data skills: Python programming, NumPy, pandas, data preparation – data collection, data cleaning, data preprocessing, data visualization; data analysis, data analytics

  • Acquire a big picture understanding of the data analyst role

  • Learn beginner and advanced Python

  • Study mathematics for Python

  • We will teach you NumPy and pandas, basics and advanced

  • Be able to work with text files

  • Understand different data types and their memory usage

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  • Learn how to obtain interesting, real-time information from an API with a simple script

  • Clean data with pandas Series and DataFrames

  • Complete a data cleaning exercise on absenteeism rate

  • Go through a complete loan data case study and apply your NumPy skills

  • Master data visualization

  • Learn how to create pie, bar, line, area, histogram, scatter, regression, and combo charts

  • Engage with coding exercises that will prepare you for the job

  • Practice with real-world data

  • Solve a final capstone project

Requirements:

  • No prior experience is required. We will start from the very basics

  • You’ll need to install Anaconda. We will show you how to do that step by step

Description:

The problem

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Most data analyst, data science, and coding courses miss a critical practical step. They don’t teach you how to work with raw data, how to clean, and preprocess it. This creates a sizeable gap between the skills you need on the job and the abilities you have acquired in training. Truth be told, real-world data is messy, so you need to know how to overcome this obstacle to become an independent data professional.

The bootcamps we have seen online and even live classes neglect this aspect and show you how to work with ‘clean’ data. But this isn’t doing you a favour. In reality, it will set you back both when you are applying for jobs, and when you’re on the job.

The solution

Our goal is to provide you with complete preparation. And this course will turn you into a job-ready data analyst. To take you there, we will cover the following fundamental topics extensively.

  • Theory about the field of data analytics
  • Basic Python
  • Advanced Python
  • NumPy
  • Pandas
  • Working with text files
  • Data collection
  • Data cleaning
  • Data preprocessing
  • Data visualization
  • Final practical example

Each of these subjects builds on the previous ones. And this is precisely what makes our curriculum so valuable. Everything is shown in the right order and we guarantee that you are not going to get lost along the way, as we have provided all necessary steps in video (not a single one skipped). In other words, we are not going to teach you how to analyse data before you know how to gather and clean it.

So, to prepare you for the entry-level job that leads to a data science position – data analyst – we created The Data Analyst Course.

This is a rather unique training program because it teaches the fundamentals you need on the job. A frequently neglected aspect of vital importance.

Moreover, our focus is to teach topics that flow smoothly and complement each other. The course provides complete preparation for someone who wants to become a data analyst at a fraction of the cost of traditional programs (not to mention the amount of time you will save). We believe that this resource will significantly boost your chances of landing a job, as it will prepare you for practical tasks and concepts that are frequently included in interviews.

The topics we will cover

1. Theory about the field of data analytics

2. Basic Python

3. Advanced Python

4. NumPy

5. Pandas

6. Working with text files

7. Data collection

8. Data cleaning

9. Data preprocessing

10. Data visualization

11. Final practical example

1. Theory about the field of data analytics

Here we will focus on the big picture. But don’t imagine long boring pages with terms you’ll have to check up in a dictionary every minute. Instead, this is where we want to define who a data analyst is, what they do, and how they create value for an organization.

Why learn it?

You need a general understanding to appreciate how every part of the course fits in with the rest of the content. As they say, if you know where you are going, chances are that you will eventually get there. And since data analyst and other data jobs are relatively new and constantly evolving, we want to provide you with a good grasp of the data analyst role specifically. Then, in the following chapters, we will teach you the actual tools you need to become a data analyst.

2. Basic Python

This course is centred around Python. So, we’ll start from the very basics. Don’t be afraid if you do not have prior programming experience.

Why learn it?

You need to learn a programming language to take full advantage of the data-rich world we live in. Unless you are equipped with such a skill, you will always be dependent on other people’s ability to extract and manipulate data, and you want to beindependent while doing analysis, right? Also, you don’t necessarily need to learn many programming languages at once. It is enough to be very skilled at just one, and we’ve naturally chosen 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

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We will introduce advanced Python topics such as 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 is independent on the job. You will be able to use Python’s core strengths to your advantage. So, here it is not just about the topics, it is also about the depth in which we explore the most relevant Python tools.

4. NumPy

NumPy is Python’s fundamental package for scientific computing. It has established itself as the go-to tool when you need to compute mathematical and statical operations.

Why learn it?

A large portion of a data analyst’s work is dedicated to preprocessing datasets. Unquestionably, this involves tons of mathematical and statistical techniques that NumPy is renowned for. In addition, the package introduces multi-dimensional array structures and provides a plethora of built-in functions and methods to use while working with them. In other words, NumPy can be described as a computationally stable state-of-the-art Python instrument that provides flexibility and can take your analysis to the next level.

5. Pandas

The pandas library is one of the most popular Python tools that facilitate data manipulation and analysis. It is very valuable because you can use it to manipulate all sorts of information – numerical tables and time series data, as well as text.

Why learn it?

Pandas is the other main tool an analyst needs to clean and preprocess the data they are working with. Its data manipulation features are second to none in Python because of the diversity and richness it provides in terms of methods and functions. The combined ability to work with both NumPy and pandas is extremely powerful as the two libraries complement each other. You need to be capable to operate with both to produce a complete and consistent analysis independently.

6. Working with text files

Exchanging information with text files is practically how we exchange information today. In this part of the course, we will use the Python, pandas, and NumPy tools learned earlier to give you the essentials you need when importing or saving data.

Why learn it?

In many courses, you are just given a dataset to practice your analytical and programming skills. However, we don’t want to close our eyes to reality, where converting a raw dataset from an external file into a workable Python format can be a massive challenge.

7. Data collection

In the real world, you don’t always have the data readily available for you. In this part of the course, you will learn how to retrieve data from an API.

Why learn it?

You need to know how to source your data, right? To be a well-rounded analyst you must be able to collect data from outside sources. This is rarely a one-click process. This section aims at providing you with all the necessary tools to do that on your own.

8. Data cleaning

The next logical step is to clean your data. This is where you will apply the pandas skills acquired earlier in practice. All lessons throughout the course have a real-world perspective.

Why learn it?

A large part of a data analyst’s job in the real world involves cleaning data and preparing it for the actual analysis. You can’t expect that you’ll deal with flawless data sources, right? So, it will be up to you to overcome this stage and clean your data.

9. Data preprocessing

Even when your dataset is clean and in an understandable shape, it isn’t quite ready to be processed for visualizations and analysis just yet. There is a crucial step in between, and that’s datapreprocessing.

Why learn it?

Data preprocessing is where a data analyst can demonstrate how good or great they are at their job. This stage of the work requires the ability to choose the right statistical tool that will improve the quality of your dataset and the knowledge to implement it with advanced pandas and NumPy techniques. Only when you’ve completed this step can you say that your dataset is preprocessed and ready for the next part, which is data visualization.

10. Data visualization

Data visualization is the face of data. Many people look at the data and see nothing. The reason for that is that they are not creating good visualizations. Or even worse – they are creating nice graphs but cannot interpret them accurately.

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Why learn it?

This part of the course will teach you how to use your data to produce meaningful insights. At the end of the day, data charts are what conveys the most information in the shortest amount of time. And nothing speaks better than a well crafted and meaningful data visualization.

11. Practical example

The course contains plenty of exercises and practical cases. In the end, we have included a comprehensive practical example that will show you how everything you have learned along the way comes nicely together. This is where you will be able to appreciate how far you have come in your journey to becoming a data analyst and starting your data career.

What you get

  • A program worth $1,250
  • Active Q&A support
  • All the knowledge to become a data analyst
  • A community of aspiring data analysts
  • A certificate of completion
  • Access to frequent future updates
  • Real-world training
  • Get ready to become a data analyst from scratch

Why wait? Every day is a missed opportunity.

Click the “Buy Now” button and become a part of our data analyst program today.

Who this course is for:

  • You should take this course if you want to become a Data Analyst and Data Scientist
  • This course is for you if you want a great career
  • The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

Course Details:

  • 20.5 hours on-demand video

  • 14 articles

  • 132 downloadable resources

  • Full lifetime access

  • Access on mobile and TV

  • Certificate of completion

The Data Analyst Course: Complete Data Analyst Bootcamp 2022 udemy courses

Complete Data Analyst Training: Python, NumPy, Pandas, Data Collection, Preprocessing, Data Types, Data Visualization
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  • Data Cleaning
(Video) Learn data analytics skills in 3 months, step by step | Complete data analyst roadmap in 2022

FAQs

Can I become data analyst in 3 months? ›

Can I become data analyst in 3 months? Ans: Make the most of your three months and learn everything you can. Because time is limited, the emphasis should be on learning Excel, SQL, R/ Python, Tableau/ PowerBI, and ML if time allows. Investing your time in projects will also give you an advantage when applying for jobs.

How long does it take to complete data analytics course? ›

Depending on the individual's learning pace, it may take you around six months to learn data analytics.

Is data analyst course easy? ›

Because the skills needed to perform Data Analyst jobs can be highly technically demanding, data analysis can sometimes be more challenging to learn than other fields in technology.

Is data analysis course hard? ›

Data analysis is neither a “hard” nor “soft” skill but is instead a process that involves a combination of both. Some of the technical skills that a data analyst must know include programming languages like Python, database tools like Excel, and data visualization tools like Tableau.

Is 40 too old to become a data analyst? ›

So despite industry ageism, a recent study by Zippia showed that the average age of data analysts in the U.S. is 43 years old. This takes us back to our titular question: are you too old to start a new career in data analytics? The short answer, in our opinion, is no.

Can you make 100k as a data analyst? ›

Candidates with advanced skills or at least three years of work experience on their resume can earn an average salary of over $100,000 per year.

Can I become a data analyst in 2 months? ›

I can take anywhere from several months to several years to become a data analyst. The amount of time it takes you will depend on your current skill set, what type of educational path you choose, and how much time you spend each week developing your data analytics skills.

Is data analytics bootcamp worth it? ›

Are data science boot camps worth it? Thousands would say yes. For many students, boot camps provide one of the only viable paths into an industry that would otherwise require years of costly study. A college education just isn't feasible for people on a budget or those looking to pivot careers.

Can I learn data analytics in 2 months? ›

Honestly saying in 2 months you can only understand WHAT is data science and HOW it is applied in industries to generate data-driven insights. To roughly understand Data Science you need at least 6 to 8 months and to become a Data Scientist you need 1 more month to build your resume and hunt for the job.

Is data analytics math heavy? ›

While data analysts need to be good with numbers, and a foundational knowledge of Math and Statistics can be helpful, much of data analysis is just following a set of logical steps. As such, people can succeed in this domain without much mathematical knowledge.

Is data analyst math hard? ›

As with any scientific career, data analysts require a strong grounding in mathematics to succeed. It may be necessary to review and, if necessary, improve your math skills before learning how to become a data analyst.

Can I become data analyst by myself? ›

It's definitely possible to become a data scientist without any formal education or experience. The most important thing is that you have the drive to learn and are motivated to solve problems.

What is the hardest part in data analysis? ›

Problem definition is hard

There are many reasons why problem definition can be hard. It is sometimes due to stakeholders who don't know what they want, and expect data scientists to solve all their data problems (either real or imagined).

Is data analyst stressful? ›

Several data professionals have defined data analytics as a stressful career. So, if you are someone planning on taking up data analytics and science as a career, it is high time that you rethink and make an informed decision.

Does data analyst require coding? ›

Do Data Analysts Code? Some Data Analysts do have to code as part of their day-to-day work, but coding skills are not typically required for jobs in data analysis.

Is data analyst A 9 5 job? ›

Generally speaking, Data Analysts can expect to work between 40 and 60 hours a week, typically on a Monday through Friday schedule, which would correspond with the hours the business or company is open. This often means a 9-5 or 8-6 day.

Can I become a data analyst at 55? ›

You can become a data scientist at any age if you're willing to put in the work.

Can I become a data analyst at age 50? ›

No, 50 is not too old to become a data scientist.

It's never too late to become a data scientist - as long as you've got the right skills and determination, you can become a data scientist at any age. Assuming you have the skillset, there isn't an age limit - even if you're starting from scratch with a degree.

Which company pays highest to data analyst? ›

Top Companies for Data Analysts in the US
Name of the CompanyAverage Salary per year
Target$132,122
Facebook$130,180
Pacific Gas and Electric Company (PG&E)$127,159
Citizens$126,312
6 more rows

Which company pays most for data analyst? ›

Industries that pay the best data analyst salaries are the ones that benefit the most out of big data analytics.
...
Technology.
CompanyAverage (In USD)Range (In USD)
Google99,50065,700-120,000
Apple95,80069,000-190,000
Meta123,00096,000-150,000
Microsoft93,06011,000-226,000
1 more row
8 Mar 2022

What is the minimum salary of data analyst? ›

According to PayScale, the average pay structure, based on experience, for Data Analysts is as follows: Entry Level (<1 yr of experience): ₹3,56,363 p.a. Early Career (1–4 yrs of experience): ₹5,20,000 p.a. Mid-career (5–9 yrs of experience): ₹8,50,000 p.a.

How do I become a data analyst with no experience? ›

How to Become a Data Analyst in 2022 With No Experience
  1. Understand where you want to go as a data analyst.
  2. Receive foundational training and understand which skills you need to acquire.
  3. Obtain the skills through a degree, bootcamp, or self-direct learning.
  4. Break into the chosen industry.

Can I be a data analyst without a degree? ›

You don't need a full-blown degree to become a data analyst, but you do need a structured and formal approach to learning the necessary skills. The best (and most flexible) way to do so is through a project-based course.

How many hours a week does a data analyst work? ›

As a data analyst, you should expect to work regular business hours in a week. Typically, this can be from 40 to 60 hours per week.

Can you get a data analyst job with a bootcamp? ›

Wondering if you'll be able to get a data analyst job after completing a Data Analytics Bootcamp? Read our study of market and graduate income data; signs point to Yes. Data Analytics is quickly becoming a generally applicable skill.

Can I become a data analyst with a bootcamp? ›

If you're keen to start a career as a data analyst, a top quality bootcamp can offer you a direct route into the field.

Can you get a job after a data science bootcamp? ›

Will a data science bootcamp get you a job? Yes. In fact, 74% to 90% of bootcamp graduates land a job within six months of graduation.

Is 2 months enough for SQL? ›

It should take an average learner about two to three weeks to master the basic concepts of SQL and start working with SQL databases. But in order to start using them effectively in real-world scenarios, you'll need to become quite fluent; and that takes time.

Is data analytics easier than programming? ›

Like with most disciplines, data science comes easier to some people than others. If you enjoy statistics and analytical thinking, you may find data science easier than software engineering. If you have a great deal of experience with programming and enjoy solving problems, you may find software engineering easier.

Is the Google data analytics certificate worth it? ›

The Google Data Analytics Professional Certification is definitely worth the time and effort. It's one of the most valuable entry-level certifications to pursue starting a career as a data analyst.

Can I be a data analyst if I hate math? ›

One popular question that we always get asked is: “Dr. Lau, can I become a data scientist or data analyst if I am not good with math or statistics?” Well, Dr. Lau's reply is always yes you can.

Can you be a data scientist if you're not good at math? ›

Being mathematically gifted isn't a strict prerequisite for being a data scientist. Sure, it helps, but being a data scientist is more than just being good at math and statistics. Being a data scientist means knowing how to solve problems and communicate them in an effective and concise manner.

What are skills required for data analyst? ›

While data analysts should have a foundational knowledge of statistics and mathematics, much of their work can be done without complex mathematics. Generally, though, data analysts should have a grasp of statistics, linear algebra, and calculus.

Do analysts get paid well? ›

Do Data Analysts Make Good Money? Yes, data analysts do make a lot of money. According to the Bureau of Labor Statistics (BLS), the average salary across all occupations in the US is $58,260. The average salary of a data analyst is well above that.

Is data analytics a good career? ›

Skilled data analysts are some of the most sought-after professionals in the world. Because the demand is so strong, and the supply of people who can truly do this job well is so limited, data analysts command huge salaries and excellent perks, even at the entry-level.

How do I get my first job as a data analyst? ›

You can learn skills and hone important data analytics skills. Reach out to your network, businesses or take up freelancing projects to do some near real-world analytics work. The whole point is to equip yourself with the skills demanded by a business rather than picking up courses while job hunting.

Can I become a data analyst in 6 months? ›

With the right mix of skills and experience, you can become a data analyst in just six months. So if you're ready to make the switch, now is the time to start learning and building your experience.

What are the 4 main types of data analytics? ›

Modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive.

Is Python for data analysis hard? ›

It's Easy to Learn

Thanks to Python's focus on simplicity and readability, it boasts a gradual and relatively low learning curve. This ease of learning makes Python an ideal tool for beginning programmers.

What is the easiest data analysis? ›

Tableau. Tableau is among the most easy-to-learn Data analytics tools that effectively slice and dice your data and create great visualizations and dashboards. Tableau can create better visualizations than Excel and can most definitely handle much more data than Excel can.

What do data analysts do all day? ›

A data analyst gathers, cleans, and studies data sets to help solve problems. Here's how you can start on a path to become one. A data analyst collects, cleans, and interprets data sets in order to answer a question or solve a problem.

Are data analysts happy? ›

According to Payscale reviews from 3,118 respondents, data analysts report a job satisfaction rating of 3.75 out of five.

Do data analysts make a lot? ›

What is an average data analyst's salary? The average base pay for a data analyst in the United States in December 2022 is $63,865, according to job listing site Glassdoor [1].

Is basic Python enough for data analyst? ›

Python Programming

Strong knowledge of programming is necessary when analysing data. In many cases, the likes of Excel can't cope with the large amounts of data that businesses have available to them. This is why programming in Python is an important skill for a Data Analyst.

Can I be a data analyst with just Python? ›

There's no wrong choice when it comes to learning Python or R. Both are in-demand skills and will allow you to perform just about any data analytics task you'll encounter.

Is SQL required for data analyst? ›

Why Is SQL Important for Data Analysis? SQL is the language used to interact with relational databases. Since most systems today capture the data using one or more databases (like MySQL, Oracle, Redshift, SQL Server, etc.), you need to know SQL to extract data from these systems and then work with it.

Can I become data analyst in 2 months? ›

I can take anywhere from several months to several years to become a data analyst. The amount of time it takes you will depend on your current skill set, what type of educational path you choose, and how much time you spend each week developing your data analytics skills.

Can I become data analyst in 4 months? ›

Remember, data analysis is a field that people spend their entire lives trying to learn. Even the individual skills required to become an analyst can take a lifetime to learn, so it is impossible to master in just a few months.

What is the fastest way to become a data analyst? ›

Complete a data analytics certification

You don't need a full-blown degree to become a data analyst, but you do need a structured and formal approach to learning the necessary skills. The best (and most flexible) way to do so is through a project-based course.

Do Data Analysts work 8 hours a day? ›

Generally speaking, Data Analysts can expect to work between 40 and 60 hours a week, typically on a Monday through Friday schedule, which would correspond with the hours the business or company is open. This often means a 9-5 or 8-6 day.

Can I become a data analyst at 50? ›

You can become a data scientist at any age if you're willing to put in the work.

Are data analytics bootcamps worth it? ›

Are data science boot camps worth it? Thousands would say yes. For many students, boot camps provide one of the only viable paths into an industry that would otherwise require years of costly study. A college education just isn't feasible for people on a budget or those looking to pivot careers.

Is data analyst a good salary? ›

According to Payscale, in the US, newly-qualified data analysts (i.e. those with less than a year of experience) earn an average annual income of $56,590. In a data analyst's first four years, this figure can rise to around $61,234.

Can everyone become data analyst? ›

Most entry-level data analyst jobs require a bachelor's degree, according to the US Bureau of Labor Statistics [1]. It's possible to develop your data analysis skills—and potentially land a job—without a degree. But earning one gives you a structured way to build skills and network with professionals in the field.

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