Data Science with Python Training in Noida, India

Get the ability to analyze data with Python using basic to advanced concepts

  • 40 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms
Group Discount

Description

Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.

Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.

This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.
Extensive hands-on labs and an interview prep will help you land lucrative jobs.


What You Will Learn

Prerequisites

There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

  • Those Interested in the field of data science
  • Those looking for a more robust, structured Python learning program
  • Those wanting to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researchers

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

Hands-on:  No hands-on

Learning Objectives:

In this module you will learn how to install Python distribution - Anaconda,  basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study

Hands-on:

  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.

Topics Covered:

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing 

Hands-on:

Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.

You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.

Topics Covered:

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Hands-on: 

  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.

Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.

Topics Covered:

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on: 

  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.

Topics Covered:

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price

Hands-on:  

  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on:

 Project to be selected by candidates.

Projects

Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science

Harvard Business Review called Data Scientist the sexiest job of the 21st Century in 2012. Data is everywhere around us and Data-driven decision making is the need of the hour. From making effective business decisions to classifying target audiences, data science offers great value to businesses. Noida bustles with some of the best companies to work for, including Paytm, Cadence Design Systems, Adobe, Ericsson, etc. All these companies are looking for expert data scientists to help them take informed decisions based on their findings. 

Below are the top technical skills required to become a data scientist: 

  1. Python Coding: Python is one of the most popular programming languages. Owing to the versatility as well as the simplicity that Python offers, it takes various formats of data and helps in the processing of this data. 
  2. R Programming: Knowledge of R programming and excellent analytical tools is usually an advantage for data scientists in order to make any data science problem easier to solve.
  3. Hadoop Platform: Though not a requirement for data science, Hadoop Platform is heavily preferred in projects. It is listed as the leading skill requirement for a data science engineer on job portals.
  4. SQL database and coding: SQL is a language that is specifically designed to help data scientists access, communicate as well as work on data. MySQL also possesses concise commands that save time and decrease the level of technical skills required to perform operations.
  5. Machine Learning and Artificial Intelligence: Proficiency in the areas of Machine Learning and Artificial Intelligence is now a prerequisite for the pursuit of a career in Data Science. Topics like Neural Learning and Decision Trees are going to be integral in this field. 
  6. Apache Spark: One of the most popular data sharing technologies worldwide, Apache Spark is a big data computation, not unlike Hadoop. The only difference between Apache Spark and Hadoop is that Apache Spark is faster, because of the fact that Hadoop reads and writes to the disk, whereas Spark makes caches of its computations in the system memory.
  7. Data Visualization: A data scientist is expected to be able to visualize the data with the help of Visualization tools. This will help convert data into a format that is easy to understand and comprehend. 
  8. Unstructured data: It is important for a data scientist to be able to work with unstructured data, which is content that is not labelled and organized into database values.

Below are the behavioural traits of a successful Data Scientist -

  • Curiosity – As you will be dealing with massive amounts of data every single day, you should have an undying hunger for knowledge to keep you going. 
  • Clarity – Whether you are cleaning up data or writing code, you should know what you are doing and why you're doing it. 
  • Creativity - Creativity in data science allows you to figure out what's missing and what needs to be included in order to get results. 
  • Scepticism – Data scientists need scepticism to keep their creativity in check, so that they do not get carried away and lose focus.

 Here are the 5 proven benefits of being a Data Scientist in Noida:

  1. High Pay: Due to high demand and low supply, data scientist jobs are one of the highest paying jobs in the IT industry today. The average salary for a Data Scientist is INR 6,65,636 per year in Noida, Uttar Pradesh.
  2. Good bonuses: Data scientists can also expect impressive bonuses and perks in their job.
  3. Education: By the time you become a data scientist, you would probably be having either a Masters or a PhD due to the demand for knowledge in this field. You might even receive offers to work as a lecturer or as a researcher for governmental as well as private institutions.
  4. Mobility: Many businesses that collect data are mostly located in developed countries. Getting a job in one would fetch you a hefty salary as well as raise your standard of living.
  5. Network: Your involvement in the tech world through research papers in international journals, tech talks at conferences and many more platforms would help expand your network of data scientists. 

Data Scientist Skills and Qualifications

Below is the list of business skills needed to become a data scientist: 

  1. Analytic Problem-Solving – You need a clear perspective and the know-how of the field you are in. Because in order to find a solution, it is important to first understand and analyse what the problem is.
  2. Communication Skills – Communicating customer analytics or deep business to companies is one of the key responsibilities of data scientists.
  3. Intellectual Curiosity – You must possess the desire to ask questions in order to produce value to the organization.
  4. Industry Knowledge – This is perhaps one of the most important skills. Having a sound knowledge of the data science industry will give you a better idea of what needs attention.

Following are some of the ways to brush up your data science skills:

  • Boot camps: Boot camps ensure you thoroughly master and understand Data Science concepts in a mere four-five days. 
  • MOOC courses:  MOOCs are the online courses that help learners polish their implementation skills in the form of assignments.
  • Certifications: You can demonstrate your knowledge and skills through certifications that are recognised in the industry. Some renowned data science certifications:
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
  • Projects: Projects help you explore already answered questions in your own way. Experiment around and polish your skills at your desired pace.
  • Competitions: Competitions improve your problem-solving skills in real-world situations. One such competition is Kaggle.  

We live in a world of data. Noida was ranked as the Best City in Uttar Pradesh and is home to several leading companies like HCL Technologies, Monotype, Tata Consultancy Services, Tech Mahindra, Infogain, Paytm, etc. Whether it’s a startup or an MNC, Data is valuable to all these companies as it tells them about their audience’s interests, allowing them to improve their customers’ experiences. So, all these companies are looking for skilled data scientists to do the job.

We’ve compiled a list of data sets you can practice on, categorized on the basis of difficulty:

  • Beginner Level
    • Iris Data Set: [4 columns,50 rows] This is the best data set for beginner to embark on their journey in the field of Data Science. The Iris data set is said to be the easiest data set to incorporate during your learning of various classification techniques. Practice Problem: The problem is using these parameters to predict the class of the flowers. 
    • Loan Prediction Data Set: [13 columns, 615 rows] The learner will have to work with concepts applicable in banking and insurance including the variables that affect the outcome, the implemented strategies and the challenges faced.Practice Problem: The problem is to predict if the loan will be approved or not. 
    • Bigmart Sales Data Set: [12 variables, 8523 rows] The Retail sector uses this data set for operations like Product Bundling, offering customizations and inventory management.Practice Problem: The problem is predicting the sales of the retail store. 
  • Intermediate Level:
    • Black Friday Data Set: [12 columns, 550,069 rows] The Black Friday Data Set comprises of sales transactions that were captured from a retail store. It helps gain an understanding of the day to day shopping experiences of millions of customers. Practice Problem: The problem is predicting the total amount of purchase.
    • Human Activity Recognition Data Set: [561 columns, 10,299 rows] The set has a collection of 30 human subjects collected via recordings.
      Practice Problem: The problem is the prediction of the category of human activity.
    • Text Mining Data Set: [30,438 rows and 21,519 columns] This data set consists of aviation safety reports describing the problems encountered on certain flights.
      Practice Problem: The problem is the classification of documents based on their labels. 
  • Advanced Level:
    • Urban Sound Classification: [8732 sound clippings, 10 classes] The Urban Sound Classification data set deals with the implementation of Machine Learning concepts to real world problems. It also includes the concepts of audio processing.
      Practice Problem: The problem is the classification of the sound obtained from specific audio. 
    • Identify the digits data set: This data set comprises of 7000 images of 31MB and 28X28 dimensions. This data set helps you in studying, analyzing, and recognizing elements present in a particular image.
      Practice Problem: The problem is identifying the digits present in an image. 
    • Vox Celebrity Data Set: This data set consists of 100,000 words spoken by 1,251 celebrities from around the world. The Vox Celebrity Data Set is meant for large scale speaker identification.
      Practice Problem: The problem is the identification of the voice of a celebrity.

How Can I Become A Data Scientist in Noida, India

Below are the steps to becoming a successful data scientist in Noida:

  1. Getting started: First things first, choose a language you are comfortable with. Most people go for Python or R Language.
  2. Mathematics and statistics: Both subjects form the backbone of Data Science. You need to have a good understanding of basic algebra and statistics.
  3. Data visualization: It is important to learn data visualization in order to communicate better with the end users in terms of explaining the data in a coherent and informed way.
  4. ML and Deep learning: Having deep learning skills to go along with basic ML skills is a must as it is through deep learning and ML techniques that you will be able to analyse the data given to you.

We have compiled a list of needed key skills & steps required to get started in this direction:

  1. Degree/certificate: Be it an online or offline classroom course, it is important to start with a basic course that covers the fundamentals. More importantly, it boosts up your resume. 
  2. Unstructured data: The job of a data scientist boils down to discovering patterns in data. Usually, the data is unstructured and doesn’t fit into a database. Your job is to understand and manipulate this unstructured data.
  3. Software and Frameworks: It is essential that you are comfortable in using some of the most popular and useful software and frameworks to go along with an equally important programming language - preferably R.
  4. Machine learning and Deep Learning: Data scientists must have knowledge of ML and Deep learning in order to make their mark in the Data Science world.
  5. Data visualization: A data scientist’s job is to make sense of huge amount of data given for analysis and provide it to the business in the form of graphs and charts. Some of the tools used for this purpose include matplotlib, ggplot2 etc.

A degree is helpful because of the following – 

  • Networking – While pursuing the degree, you will get the opportunity to make friends and acquaintances from the same industry.
  • Structured learning – Following a particular schedule and keeping up with the curriculum is more effective and beneficial than doing things unplanned.
  • Internships – Internships are an excellent opportunity to gather hands-on training, experience a working environment and make acquaintances at the same time.
  • Recognized academic qualifications for your résumé – A degree from a prestigious institution will not only look good but will also give you a head start in the race for the top jobs.

If your score is more than 6 points, you should get a Master’s degree:

  • A strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • A weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
  • A non-STEM background: 5 points
  • Less than 1 year of experience in Python: 3 points
  • No experience of a job that requires regular coding: 3 points
  • Independent learning is not your cup of tea: 4 points
  • Cannot understand that this scorecard is a regression algorithm: 1 point

Yes, programming knowledge is a must in the field of Data Science because of the following reasons:

  • Data sets: Programming knowledge helps you make sense out of structured and unstructured data sets.
  • Statistics: If a data scientist has knowledge about statistics but has no idea how to implement this knowledge, the knowledge of statistics becomes much less useful in his/her application of data science in his/her field of work.
  • Framework: The programming ability of a data scientist also enables him/her to perform data science in a proper and efficient manner. This also enables a data scientist to build systems that an organization can make use of in order to create frameworks to automatically analyse data.

Data Scientist Jobs in Noida, India

If you want to get a job in the field of Data Science, you need to follow this path:

  • Getting started: Understand what data science actually means and the roles and responsibilities of a data scientist. Then choose a language you are familiar with.
  • Mathematics: Data science is all about making sense of raw data, finding patterns and relationships between them and finally representing them, which is why it is crucial that you have a sound grasp of the subject.
  • Libraries: Data science process involves various tasks ranging from pre-processing the data given to plotting the structured data and finally to applying ML algorithms as well. Some famous libraries are:
    • Scikit-learn
    • SciPy
    • NumPy
    • Pandas
    • ggplot2
    • Matplotlib
  • Data visualization: It’s your job to make sense of the data given to you by finding patterns and making it as simple as possible. The most popular way to visualize data is by creating a graph. There are various libraries that can be used for this task:
    • Matplotlib - Python
    • Ggplot2 - R
  • Data pre-processing: Due to the unstructured form of data, it becomes necessary for data scientists to pre-process this data in order to make it analysis-ready. After pre-processing, our data would be in a structured form and ready to be injected into ML tool for analysis.
  • ML and Deep learning: Having deep learning skills to go along with basic ML skills on the CV is a must for every data scientist. For data analysis, deep learning is highly preferred as deep learning algorithms are designed to work when you have to deal with a huge set of data. 
  • Natural Language processing: Data Scientists need to have the required knowledge of processing the text form of data and further classifying it. 
  • Polishing skills: Competitions provide an opportunity for you to learn and exhibit your skills. You can also explore the field by experimenting and creating your own projects. 

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 expand your professional connections.
  • Competitions: Competitions are a great way to exhibit your skills to potential employers. 
  • Referral: According to a recent survey, referrals are the primary source of interviews in data science companies. Make sure your profiles on all the job portals are 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 interview.

The data generated every day is a gold mine of patterns and ideas that could prove to be very helpful for making key business decisions. It is the responsibility of a data scientist to extract the relevant information and make sense of it.

Data Scientist Roles & Responsibilities:

  • Fetching data that is relevant to the business from the huge amount of data provided to them.
  • Organize and analyze the data.
  • Creation of Machine Learning techniques, programs, and tools in order to make sense of the data.
  • Perform statistical analysis on data sets with the aim of predicting future outcomes or taking informed decisions.

The average salary for a Data Scientist is ₹ 6,65,636 per year in Noida, Uttar Pradesh.

A career path in the field of Data Science in Noida 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.

Data Mining Engineer: A Data Mining Engineer is an individual who has the job of examining the data for the needs of the business. He also needs to create sophisticated algorithms that further aid in the analysis of data.

Data Architect: The role of 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.

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, so as to explore patterns from the given data. 

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

Noida is fast becoming an employment sector where Data Science can flourish. Below are the top professional organizations for data scientists – 

Referrals are the most effective way to get hired. Some of the other ways to network with data scientists are:

  • Data science conferences – Held routinely and immensely helpful.
  • Online platforms – Not just job portals but also forums for communities are a great help.
  • Social gatherings – like Meetup 

There are several career options for a data scientist in Noida today – 

  1. Data Scientist
  2. Data Architect
  3. Data Administrator
  4. Data Analyst
  5. Business Analyst
  6. Marketing Analyst
  7. Data/Analytics Manager
  8. Business Intelligence Manager

Companies generally prefer data scientists to have mastery over some software and tools. They generally look for:

  • 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: Programming is vital in data science and Python is one of the most prominent languages of programming. So, it is important to learn Python Basics before you start learning any data science libraries.
  • Machine Learning: After preparing the data, deep learning is used to analyze the patterns and find a relationship. Having ML skills is a must.
  • Projects: The best approach to learn data science is by practising with real-world projects so that you can build your portfolio.

Data Science with Python Noida, India

The simplicity of Python makes it popular among data scientists. It is a structured and object-oriented programming language that contains several libraries and packages that are useful for the purposes of Data Science. The Python community is another big advantage. There are millions of developers working on the same problems with the same programming language every day. They form forums, communities and clubs to interact with each other and help solve problems.

Below are the most popular programming languages used in the Data Science field apart from Python:

  • R: It has a steep learning curve, but it does offer various advantages:
    • The big open-source community directly facilitates great open source packages.
    • Includes loads of statistical functions and handles matrix operations smoothly.
    • Via ggplot2, R provides us with a great data visualization tool.
  • SQL: SQL is a structured query language which works upon relational databases. It has following advantages:
    • Pretty readable syntax.
    • Useful in updating and manipulating relational databases.
  • Java: Even though it has less number of libraries and its verbosity is limited, it has many advantages as well, such as:
    • Compatibility. Due to already systems coded in Java at backend, it is easier to integrate java data science projects to it.
    • It is a high-performance, general purpose, and a compiled language.
  • Scala: It is popular in data science field even though it has a complex syntax because of the following reasons:
    • As it runs on JVM, any Scala program can run on Java as well.
    • Delivers high-performance cluster computing when paired with Apache Spark.

Following are the steps to install Python 3 on windows:

  • Download and setup: Visit the download page to setup Python on Windows. 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 the terminal.

  • You can use Anaconda to do the same 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

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

To install python 3 on Mac OS X, follow the below steps:

  • Install xcode: You will 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 https://raw.githubusercontent.com/Homebrew/install/master/install)"

Confirm the same by typing: brew doctor

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

brew install python

To confirm its version, use: python --version

Note: It’s advisable to install virtualenv, which will help you create isolated places to run different projects and may run even on different python versions.

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FAQs

The Course

Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.

 Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

  • Get advanced knowledge of data science and how to use them in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Learn tools like Python

By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.

Tools and Technologies used for this course are

  • Python
  • MS Excel

There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Python and data science experts who have years of industry experience. 

Finance Related

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

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Data Science with Python Certification Course in Noida

An acronym of New Okhla Industrial Development Authority, Noida is a city that was established as part of a planned urbanization thrust in the 1970s.One of the most well planned cities in India, this city has the highest per capita income in the national capital region. This makes it a highly sought-after hot spot for realty, IT and IT services and BPOs. The infrastructural facilities in Noida are state-of-the-art and on par with global standards, and this city may soon surpass Bangalore as the acknowledged software capital of the country. Many top multinationals outsourcing IT services are located here- including Sapient, Headstrong, TCS, Fujitsu, Adobe among others. Due to the multifold advantages it offers, the city offers great scope for professionals in all areas of software technology such as Big Data and Hadoop 2.0 Developer, CEH and so on. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.