Data Science with Python Course

Our Data Science with Python course content focuses on engaging interaction. Most of the class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing promptly applicable skills to real-world problems. 

The workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years.

Our Data Science with Python workshops are currently held virtually. Anyone with steady and reliable internet access, from anywhere across the world, can access the course and make the most of it. We will have learners joining from different parts of the world in our online sessions.

Schedules for our upcoming workshops in Data Science with Python can be found here. You can pick a batch with timings that suit your schedule and availability. You will have the freedom to use the self-paced part of the program at any time that is convenient to you.

For video conferencing, we currently use the Zoom platform. We will also be adding more integrations with Webex and Microsoft Teams. Learners will not have to wait for any notifications, links, or installation of any additional software. However, all the sessions and recordings will be available right within our LMS platform when you click on the Data Science with Python complete course.

Once registered, you will receive a registration link from our LMS to your e-mail ID. You will have to visit the link and set your password. After this, you can log in to our platform and start your educational journey. Accelerate your career with the best online course for data science with python.

Yes. There will be other participants who actively participate in the class. They attend Data Science with Python training online training remotely from the office, home, or any place of their choosing. These participants could be from anywhere in the world.

Via our Help and Support section, our support team is available to learners in case of any queries 24/7. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from our LMS at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

If you have any more questions, please raise a ticket or email us at [email protected] and we will be happy to get back to you.

A Data Scientist is an individual who has the combined abilities of a mathematician, a computer scientist, and a trend spotter. The job of a Data Scientist is to decipher large volumes of data, mine the relevant parts of this data, and then analyze this data to make predictions for similar data in the future. A career path in Data Science can be explained in the following ways. 

• Business Intelligence Analyst: A Business Intelligence Analyst is an individual who has the job of figuring out the business as well as the market trends. This he/she does by the analysis of data to develop a clear picture of where exactly the business stands in the business environment. 
   

• Data Mining Engineer: A Data Mining Engineer is an individual who has the job of not only examining the data for the needs of the business, but also for the benefit of a third party. In addition to his job of the examination of data, a Data Mining Engineer also needs to create sophisticated algorithms that further aid in the analysis of data.
  

• Data Architect: The role of a Data Architect is to work in tandem with system designers, developers, and users in order to create blueprints that are used by data management systems in order to integrate, protect, maintain as well as centralize data sources.
  

• Data Scientist: The main responsibility of a Data Scientist is to pursue a business case by analysis, development of hypotheses as well as the development of an understanding of data, to explore patterns from the given data. Data Scientists then move on to the development of algorithms and systems that make use of this data in a productive manner so as to further the interests of business.
  

• Senior Data Scientist: A Senior Data Scientist is tasked with anticipating future business needs and shaping the projects, systems and data analyses of today to suit those business needs in the future. 

If you are thinking to apply for a Data Science job, then follow the below steps to increase your chances of success: 

• Study: To prepare for an interview, cover all important topics, including: 

• Probability 

• Statistics 

• Statistical models 

• Machine Learning 

• Understanding of neural networks 

• Meetups and conferences: Tech meetups and data science conferences are the best way to start building your network or expanding your professional connections. 

• Competitions: Implement, test and keep polishing your skills by participating in online competitions like Kaggle. 

• Referral: According to a recent survey, referrals are the primary source of interviews in data science companies. So, make sure your LinkedIn profile is up to date. 

• Interview: If you think you are all equipped for the interviews, then go for it. Learn from the questions that you were not able to answer and study them for the next interviews. 

Our data science with python course syllabus takes care of including all these important topics and concepts in a way that will be readily applicable in the real world.

The top professional organizations for Data Scientists are given below: 

• Data Science Association 

• International Institute for Analytics (IIA) 

• International Machine Learning Society (IMLS) 

• Institute for Operations Research and the Management Sciences (INFORMS) 

• Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) 

• AAi: Advanced Analytics Institute 

• American Statistical Association. 

• SIGKDD, ACM Special Interest Group on Knowledge Discovery in Data and Data Mining 

• National Center for Data Mining (NCDM) 

An online Data Science with Python certificate will help you establish yourself as a competent Data Scientist and make a good reputation in the discipline.

The most effective way to get hired is through referrals. Some of the other ways to network with Data Scientists are: 

• Data science conferences 

• Online platforms such as LinkedIn and others 

• Social gatherings like Meetup 

Getting a Data Science with Python professional certificate and accumulating experience in Data Science will eventually make you a sought-after Data Scientist too.

Due to high demand and low supply in the case of Data Scientists in the industry, the expectations from them are also high. However, this means that the recognition and career benefits (like salary) are exceptionally high as well. If you are aspiring to be a data scientist then we have compiled key points, which the employers generally look for in data scientists while hiring: 

• Education: Most of the data scientists are Masters and PhDs in the field so it is essential that you acquire higher education if you aim to be a data scientist. Getting certified also adds to it.
  

• Programming: Data Science is a field of computer science in general so it goes without question that your programming skills determine how well you can handle the job.
  

• Libraries/Tools: Programming languages are a basic platform on which there are libraries and tools built which help you prepare, analyze, and visualize data.
  

• Machine Learning: After preparing the data, deep learning is applied to it to analyze the patterns and find a relationship in it. Having ML skills is necessary.
  

• Projects: Projects help provide proof of your skills and determine your strong points and interests, which in turn helps you explore this field.
  

• Communication: Data scientists communicate not only within their own team of data scientists but with other non-tech people such as the Sales team, marketing team etc. who do not understand technical language. It is, therefore, imperative that a data scientist is able to explain his/her findings in a simple way.
  

Here are the key points for which employers generally look for while hiring Data Scientists: 

• Education: Data Scientists have more PhDs than any of the other job titles. So, getting a degree will be beneficial. Getting certified also adds to it.
  

• Programming: Python is a great programming language for data scientists. So, it is important to learn Python Basics before you start learning any data science libraries.
  

• Machine Learning: Having ML skills is a must. After preparing the data, deep learning is used to analyze the patterns and find a relationship.
  

• Projects: The best approach to learn Data Science is by practicing with real-world projects so that you can build your portfolio. 

Yes, we have a 100% money-back guarantee in line with our 100% Satisfaction Guarantee policy.

Yes, there are scholarships available for students and veterans with grants up to 50% of the Data Science with Python course price. 

To avail scholarships for our online Data Science with Python course, please get in touch with us at [email protected]. The team shall send across the instructions and forms to you. Based upon the responses and answers that we receive, our panel of experts take a decision on the Grant. The entire process could take about 7 to 15 days.

Yes. Group discounts are available and apply to groups as small as three (3) participants. The more participants that attend a training course, the greater the discount. You can typically save up to 20% to 30% on the course fee by registering in groups. For more details, please check out upcoming schedules.

Yes, instalment options are available for payment of Data Science with Python course fee. To avail the instalment option, please get in touch with us at [email protected]. The team will explain how the instalments work and provide timelines for your case.

Typically, the number of instalments varies from 2 to 3, but the full amount must be paid before you complete the course.

Data Science deals with identification, representation, and extraction of meaningful information, so any programming language equipped with tools to do these tasks efficiently will be naturally popular. Python is one such popular language and the reasons for the same include: 

• Short learning curve: Unlike its competitor, R, Python is comparatively easy and quick to learn due to its readable and easy-to-understand syntax. 
   

• Scalability: YouTube migrated to Python due to its efficient scaling capabilities. Compared to its competitors - R, MATLAB etc., Python has a significant lead in scalability due to the flexibility it provides during problem-solving. 
   

• Libraries: Python is the leading language for machine learning projects due to the packages it offers to the developers. Packages like pandas, scikit-learn, etc. allow for ML algorithms to be applied to the data easily. 
   

• Data visualization: With the help of matplotlib, Python enables us to plot complex data representations into 2D plots. Data visualization is a significant process in the job of a data scientist. With the help of Seaborn, ggplot etc. along with matplotlib, Python provides us with a great data visualization tool. 

This is why the best data science with python course online will be a great chance for you to acquire the skills that you need to thrive in a data science career.

As Data Science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language. 

• R: Although it has a steep learning curve, it has various advantages. 

• • The big open-source community that provides R with high-quality open-source packages. 

• • Includes loads of statistical functions and handles matrix operations smoothly. 

• • Via ggplot2, R provides us with a great data visualization tool. 

• Python: Though it has fewer packages than R, Python is still one of the most sought-after languages in the data science field. 

• • pandas, scikit-learn, and tensorflow provide most of the libraries needed for data science purposes. 

• • Easy to learn and implement it. 

• • It has a big open-source community as well. 

• SQL: SQL is a structured query language which works upon relational databases. 

• • Pretty readable syntax. 

• • Efficient at updating, manipulating and querying data in relational databases. 

• Java: Even though it has a smaller number of libraries for data science purposes and with Java’s verbosity limiting its potential, it has many advantages as well: 

• • Compatibility. Systems are already coded in Java in the backend, and it's therefore easier to integrate Java data science projects to it. 

• • It is a high-performance, general purpose, and a compiled language. 

• Scala: Scala runs on JVM and has a complex syntax. Still, it is a preferred language in data science domain due to the following advantages: 

• • As it runs on JVM, any Scala program can run on Java as well. 

• • When used along with Apache Spark, we get high-performance cluster computing.

Follow these steps to successfully install Python 3 on windows: 

• Download and setup: Go to the download page and setup your python on your windows via GUI installer. While installing, select the checkbox at the bottom asking you to add Python 3.x to PATH, which is your classpath and will allow you to use Python’s functionalities from terminal. 

Alternatively, you can also install Python via Anaconda as well. Check if Python is installed by running the following command, you will be shown the version installed: 

python --version 

• Update and install setuptools and pip: Use below command to install and update 2 of most crucial libraries (3rd party): 

python -m pip install -U pip 

Note: You can install virtualenv to create isolated Python environments and pipenv, which is a Python dependency manager.

You can simply install Python 3 from their official website through a .dmg package, but we recommend using Homebrew to install Python as well as its dependencies. To install Python 3 on Mac OS X, just follow the below steps: 

• Install xcode: To install brew, you need Apple’s Xcode package, so start with the following command and follow through it:$ xcode-select --install
  

• Install brew: Install Homebrew, a package manager for Apple, using following command:/usr/bin/ruby -e "$(curl -fsSL)"Confirm if it is installed by typing: brew doctor
  

• Install Python 3: To install latest version of python, use: brew install python
  

• To confirm its version, use: python --version 

You should also install virtualenv, which will help you create isolated places to run different projects and may run even on different python versions. 

Follow the below steps to successfully install Python 2 on your windows: 

• Download the MSI file from the official download website and go through its GUI setup. 

• Suppose you have installed Python 2.x, so windows would create a folder. 

• C:\Python2x This helps in installing multiple versions of Python on your windows machine. 

• To use python command line from terminal, go to Control Panel > System > Advanced system settings > Environment variables. Add C:\Python2x; (with semicolon) to the PATH variable value and click OK. 

• Restart the command prompt and type the following to see the installed python version: Python –version. 

Unstructured data refers to the undefined contents of a data set that cannot be fit into structured database tables. It is basically information that is not organized in a predefined manner nor has a data model that is pre-defined. Unstructured data is generally text-heavy but may also consist of other data such as numbers, facts, figures, audio, video etc. 

While unstructured data may be difficult to organize, if a company is able to tap into it in a meaningful and efficient manner, it is like digging up a bag of gold. Unstructured data can aid companies in the formation of important business decisions if a company is able to integrate this unstructured data into their information management systems and landscapes.

pandas and NumPy are two of the most used Python libraries for data manipulation. Most of the times they are used in a single project. Although pandas is a library built directly off from NumPy, there are some differences between both of them.

You cannot go wrong choosing Python as a first language whether you want to pursue Data Science or another specialty. Data Science leverages this language in all its functionalities and is one of the best languages Data Scientists can leverage. Once you get a stronghold on one programming language, it's easier to learn the other ones since many concepts overlap across different languages.

Many of the Data Science requirements can be taken care of by using Python and SQL. 

Here is an overview of their uses. 

Python in Data Science is used for: 

• Data Cleaning 

• Data Processing 

• Data Manipulation 

• Graphs and Plotting 

• Statistical Analysis 

SQL in Data Science is used for: 

• Data warehousing 

• Data pipelining 

• Data Extraction 

• CRUD Operations for Transactional Loads

While Python and SQL cover the vast variety of Data Science needs and requirements, tools and languages such as R, Apache Spark, Hadoop, Kafka, and Cloud platforms can come in handy for other important requirements. It is better to always keep upskilling yourself with the useful tools to advance in your career. 

Python is considered the most sufficient when it comes to a career in Data Science for various reasons such as rich ecosystem of libraries, ease of use, integration and flexibility, community support, deployment and production, and Data Science workflow.

Python in Data Science is used for:

• Data Cleaning

• Data Processing

• Data Manipulation

• Graphs and Plotting

• Statistical Analysis

In Python you can Learn:

• pandas

• NumPy

• Matplotlib

It would be hard to get a job just by knowing Python unless you are extremely good at it. While Python is one of the most popular languages for Data Science, it is not enough to just know Python to excel in the field. Along with Python, it is important to learn about other tools and languages like R, SQL, Hadoop, and Spark.