Data Analyst Course: Complete Data Analyst Bootcamp 2021
This Course Designed for
Complete Data Analyst Training: Python, NumPy, Pandas, Data Collection,
Preprocessing, Data Types, Data Visualization
The
problem
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
Bootcamp 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 favor. 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 the video (not a single one skipped). In other words, we are
not going to teach you how to analyze 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 centered 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 be independent 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
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 panda's 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 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 data preprocessing.
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.
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 convey 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.
What you Learn from this Course?
- 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
- 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
- Expand your knowledge of NumPy
– statistics and preprocessing
- 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
Who can Enroll in this
Course?
- 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
What Requirement for this Course?
- 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
How can Learn Course
this Course?
1.
Create
Account / Login on Udemy.com
2.
Learn
Course by Enroll in this Course
Coupon Code for This Course: 3517A9C42A4C5BDF3433
Hurry Up!
** Coupon Code Valid for
Limited Time**
0 Comments