Data Science with Python

Know your way around the Data Science with Python.

Python falls under the category of high-level programming languages. So it has a lot of very powerful features such as built-in data structures and other facilities which put it far ahead of low-level languages such as assembly languages or machine code. The syntax of Python is very sparse and minimal. Python also uses whitespace for formatting instead of curly brackets or braces. As a result, Python code is very easy to read and not obscure or intricate at all.

Python’s code is written with dynamic binding and dynamic typing instead of static binding and static typing. So writing programs with Python is usually a very smooth and pleasant experience. There are no pain points at all. So it is very suitable for Rapid Application Development or RAD – creating simulations, testing concepts, and creating prototypes quickly. Also, it is frequently used as a glue language or a scripting language to connect various components of a software application together or to write small and fast scripts. Due to these convenient features of Python Data Science has become a breeze.

Python also comes bundled with a collection of libraries which is as vast as it is comprehensive. This makes programming very convenient for software developers as they can avoid reinventing the wheel for every little task they want to perform. There also exists a vast community around Python which has developed its own collection of libraries which is quite large. Python community members hail from fields such as computer science, mathematics, statistics, information technology, information security, data science, Game development, medicine and pharmacology, and even biology and bioinformatics. So one will benefit a lot by learning Python through this Python for Data Science course.

Why is Python Popular Among Data Scientists?

All the above points make Python a very beneficial language for programmers. As a result of this, Python’s growth is the fastest among all the programming languages of the world. Many agencies predict that it will become the most popular programming language of the world within the present decade. Data Science with Python especially has become extremely common and widespread. The following are the reasons for Python’s popularity -

  1. Python has done a great job of making all its features very accessible. Its syntax has been especially designed to align with the natural way of thinking and contains many characteristics similar to natural languages used by humans. Thus it does not take much time for beginners to programming to learn Python.
  1. The Python programming language gets support from influential corporate sponsors. Facebook, Amazon Web Services, and most of all Google support the community of Python developers and the official maintainers of the Python programming language heavily. They have helped Python in numerous ways such as by providing programmers, tools, money, and organizing bug fixing marathons. They even offer free Python data science training in the form of Python data science courses to those interested. These efforts lead to Python’s improvement and help it advance and grow as a programming language.
  1. The biggest reason is perhaps the availability of a large number of Python libraries and frameworks. They make it very simple to carry out Data Science in Python. This phenomenon has become possible due to the large and supportive community that has gathered around Python. Another cause is the heavy corporate sponsorship and backing. It has led to the existence of well-designed and well-maintained libraries and frameworks catering to every possible purpose that a Python programmer might have.

Careerera’s Data Science with Python Course

With Careerera, one can learn python for data science in a very simple and straightforward manner. Careerera offers a very carefully and painstakingly designed course titled ‘Data Science with Python.’ So you can engage in learning Python for data science.   

  1. Download and run various analysis algorithms on data with the help of Python programs.
  2. Learn Python programming techniques to analyze and evaluate different kinds of data – ordinal, categorical, and encoding.
  3. Produce data visualizations such as scatter plots, polar area diagrams, time series sequences, line graphs, timelines, line graphs, tree diagrams, ring charts, sunburst diagrams, matrix charts, node link diagrams, word clouds, alluvial diagrams, scatter plots, pie charts, Venn diagrams, stacked bar graphs, histograms, flow maps, density maps, cartograms, heat maps etc.
  4. Present the results of your data analysis step by step with the help of the full set of features of the IPython notebooks.
  5. Use data science tools and techniques to carry out predictive modeling.

So now you can learn data science with Python through this Python data science online course.

Course Highlights

1)      A total of 36 hours session provided by the certified professionals.

2)      E-learning 

3)      Tips and Tricks are provided by the counsel to gain the desired outcome.

4)      Full Length Tests that consist of 250 questions that are to be completed within 4 hours.

5)      Essential reading material, updates, best website links, and the reference books for students are provided throughout the training period.

6)      A better understanding of all the modules wrapped in the curriculum is provided through a combination of contemporary approaches.

7)      Several downloadable students handouts, and the Courseware, that learners can easily refer to, are also rendered from our end.

8)      All the online videos and reading material comes along with a lifetime access option.

9)      The website comes with a user friendly interface, so even those who are technologically-challenged need not to worry.

Program Curriculum

courseID =262

See which topics you will have to assimilate.

  • 1
  • Introduction to Data Science using Python
  • 2
  • Python basics
  • 3-
  • Maths for DS-Statistics
  • 4-
  • OOPs in Python
  • 5-
  • NumPy Library In python
  • 6-
  • Scipy for scientific computing
  • 7-
  • Data manipulation
  • 8-
  • Data visualization with Matplotlib
  • 9-
  • Machine Learning using Python
  • 10-
  • Supervised learning
  • 11-
  • Unsupervised Learning
  • 12-
  • Dimensionality Reduction
  • 13-
  • Time Series Forecasting
  • 14-
  • Project

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Get the answers to your questions here.

Q1 : How long does it take to learn python for data science?

Usually a beginner can pick up Python in a couple of months. But to learn how to use Python for data science takes much longer. It can take from 6 months to a whole year to properly learn and understand the various applications of Python and common Python libraries in Data science. So the final estimate is from 6 months to 1 year.

Q2 : Is Python good for data science?

Python is extremely well-suited for data science. It’s simple syntax and powerful language features such as dynamic binding make it very useful for the field of Data Science. If a data scientist chooses Python, he won’t have to learn the nitty gritties of programming from scratch as Python is a very beginner friendly language and will give him a boost so that he becomes proficient in programming very fast. If he were to choose any other language, he would have to spend a lot of time wrestling with the language itself. As a result, he won’t be able to concentrate on his actual work related to data science.

Q3 : How do I start learning data science with Python?

A very simple and beginner-friendly way to install the Jupyter notebook is by installing the Anaconda distribution. Anaconda is the most popular data scientist bundle which is filled with preloaded data science tools, modules, and libraries.

The first step is to set up the Jupyter learning environment. It contains many useful features which help data scientists to save a lot of time and energy. This is because the features have already been preprogrammed into the Jupyter learning environment and don’t have to be implemented from scratch. Jupyter makes it possible to add plots, formulae, comments, images, and code into one document. These elements are very common in data science and thus data scientists find this feature to be invaluable. Jupyter is so flexible that it allows one to use different programming languages in the same notebook, input formulae in Latex or ASCII, generate graphs and plots, and even execute Python code.

The simplest and most beginner-friendly way to install the Jupyter learning environment is to install the Anaconda distribution. It is almost the standard data science bundle because it contains preloaded data science tools, modules, and libraries.

Q4 : Where can I practice Python for Data Science?

You can download and install the Python interpreter. It comes bundled with an IDE or an Integrated Development Environment. It is very easy and simple to run scripts on the interpreter. One can also test individual lines of code. One can load a module in it and step through every single line of code looking for bugs. So the IDE provides an ideal testing and debugging environment too.

Q5 : Can you learn python in a month?

It is difficult but it can be done. One has to be very determined as a learner though. One should also have the guidance of expert instructors who will be able to teach him the core and essential concepts in a short amount of time.

Q6 : Can I get a job with Python certification?

You can get a job very easily if you have a Python certification. Companies constantly seek skilled and competent Python developers and possessing a Python certification is a good proof that you are one. So if you get a Python certification your value in the job market will go up tremendously. In addition to that, you will be able to demonstrate to your potential employers that you have a self-driven learner’s mindset and that you hold a great amount of interest in your career. Companies will immediately give you preference over all the other candidates in the hiring pool.

Q7 : I had heard that Python is an interpreted language, and is thus slow. Why is it still used for Data Science?

One of the drawbacks of Python is that it is very slow very algorithms which perform a lot of big calculations and other straightforward tasks related to numbers. Another is that it is not able to handle large data sets efficiently or at a fast speed. So one may naturally be lead to question that why is Python the most popular programming language for Data science.

The answer is that in Python it is very easy to write bindings or extensions for other languages. So what data scientists typically do is that they offload the number crunching and data processing tasks to other faster languages such as C or Fortran. This lets them take the advantage of the speed and processing power of the faster languages while at the same time retaining the simplicity, convenience, syntax, modules, libraries, and interoperability of Python, which is a high-level language.

Q8 : Which Python libraries are commonly used in Data Science?

  1. Numpy -

The first library which an aspiring data scientist should take the time to learn is Numpy. They should make themselves absolutely conversant with its structure and functions. It is a library whose main purpose is to provide fast, heavily optimized, and efficient multidimensional arrays. Multidimensional arrays are the most frequently used and basic data structure of machine learning algorithms. So one can understand how essential it is to have a fast implementation of them. This is why most of the parts of Numpy dealing with fast computation are written in C and C++, which are very fast languages.

  1. Pandas -

The full form of Pandas is Python Data Analysis Library. As its name suggests, it is a library mainly intended to provide data scientists with the functions of data analysis and data manipulation. It is particularly known for its functions related to numerical tables and time series sequences. The basic data structure of Pandas, which has been specially designed to aid in data manipulation, is called a data frame.

  1. Matplotlib -

As a data scientist one will have to spend a large portion of their time creating data visualizations or reading them. Data visualizations offer a huge advantage over simple large arrays of data and that is that the data is presented in a format which is easily digestible. Matplotlib has been designed with this goal in mind. It is the most popular Python library for generating data visualizations from arrays and other formats of data. It can be used to create several different types of plots such as histogram, scatter, bar, line, and box plots.

Q9 : What kind of learning does Careerera provide?

We provide online instructor-led live sessions for various courses.

Q10 : What is the process of Certification?

At the end of your respective course you will be provided with a real time project by our instructor to test your practical and applying skills. Once you complete the project, it will undergo scrutiny by the instructor. Only when the instructor is satisfied with your work, you will be rewarded with your certificate.



John Carter


The Training Course Of The Data Science with Python with
all the Mandatory Course Requirements with Distinction.

Certificate ID:32480XXX

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