Data Science with Python Training in Minneapolis, MN, United States

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

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.

Meet your instructors

Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

View Profile

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

The profession of Data Scientist is considered as the sexiest job of the 21st century. The reason behind this is data. Today, tons of data is generated every day. Many companies collect this data and sell it to ad agencies so that they can use it to make their website more user friendly and earn crazy profits. In Minneapolis, MN, companies like Amazon Web Services, Enable Data, US Bank, General Mills, Accenture, Blue Buffalo, Cargill, Rich Products Corporation, etc, are eagerly looking for data scientists to join their team. These companies have started to understand the importance of decision making based on the analysis of data. Currently, tons of data is generated every single day. This data can be used to help the organization make important marketing decisions. There are not enough qualified and experienced data scientists. This allows Data Scientists who are skilled to make a handsome salary.

If you want to become a data scientist, you would need to become an expert in some skills. You can gain the knowledge to do so by getting a degree. In Minneapolis, universities like The University of Minnesota, Saint Paul College are offering a Master’s program in Data Science. Here are the 8 top technical skills required to become a Data Scientist:

  1. Python Coding: Learning Python is very important to become a data scientist. Most of the data science projects use python due to the versatility and simplicity it offers. It allows creating and performing dataset operations.
  2. R Programming: You have to have an in-depth knowledge of an analytical tool to become a top-notch data scientist. R programming makes solving statistical problems easy.
  3. Hadoop Platform: This framework is used in many data science projects. So, it would be beneficial for you if you have a complete understanding of the Hadoop platform.
  4. SQL database and coding: While working with data science, you will be dealing with databases. To communicate, access, and work on this database, you will be needing a Structured Query Language.
  5. Machine Learning and Artificial Intelligence: Machine learning skills are a must for making a career in Data Science. You should make yourself familiar with the following topics:
    • Decision trees
    • Neural Networks
    • Logistic regression
    • Machine learning algorithms
    • Reinforcement learning
    • Adversarial learning
  6. Apache Spark: Apache Spark is a framework similar to Hadoop. It can run the data science algorithms faster. It disseminates the processing of data so that data loss can be prevented. It makes a cache of its computation by using the system memory which makes it faster than Hadoop.
  7. Data Visualization: As a data scientist, it is your job to make the data as simple as possible so that the non-technical members of the team can also understand it. This is where data visualization comes into play. There are several tools like ggplot, d3.js, Tableau, and matplotlib available for the same. These tools also make grasping insights and providing outcomes easier.
  8. Unstructured data: Most of the data that is collected is unlabelled and unorganized meaning that it is in unstructured form. This includes blog posts, audios, videos, customer reviews, etc. It is the responsibility of a data scientist to structure this data.

The top 5 essential behavioral traits of a successful data science professional:

  • Curiosity – Data Science is a huge field and while performing the analysis, you must be curious enough to get answers.
  • Clarity – You need to have a clear understanding of what you are doing and why you are doing it at every point. Otherwise you might get confused along the way
  • Creativity – A data scientist must be creative to create new modeling features, develop new tools, and find new ways for visualizing the data.
  • Skepticism – Although the data science field demands creativity, a data scientist must also be skeptical so that he/she does not get carried away and stays in the real world.

There are several corporations in Minneapolis, MN that are hiring data scientists for helping them in optimizing their business. These include UnitedHealth Group, Be the Match, US Bank, UMN, General Mills, Rich Products Corporation, Accenture, Blue Buffalo Co. Ltd Corporate, etc. For a job to be as popular as that of a data scientist, there has to be some great benefits, such as:

  • High Pay: Since there are not enough experienced data scientists, it has led to Data Scientist becoming one of the highest paid professionals in the IT industry.
  • Good bonuses: Getting a job as a Data Scientist comes with several perks like signing bonus, equity shares, etc.
  • Education: You will have to get a Master’s degree or a PhD to become a data scientist. This also allows you to work as a lecturer or a researcher in an institute.
  • Mobility: There are several organizations based in developed countries that are looking to hire data scientists. Getting a job at such a place will be accompanied with a handsome salary and improved living standards.
  •  Network: There are several conference, meetups, and tech talks organized for data scientists that can help them build a network and expand their professional connections.

Data Scientist Skills & Qualifications

Some of the business skills that are needed to become a data scientist are:

  1. Analytical skills: Understanding and analysis of the problem is important in order to find the right solution. For that, clarity and strategy awareness is necessary.
  2. Communicative skills: A key role for a data scientist is to communicate deep business and customer analytics to companies.
  3. Curiosity of the intellect: You need to be curious to find answers to problems. Undying curiosity and the ability to deliver results are always valued by businesses.
  4. Knowledge of the industry: Lastly, this is one of the most vital skills. A good industry knowledge provides clearer idea about what should be paid attention to.

The 5 best ways to brush up your Data Science skills to get a job as a Data Scientist are:

  • Boot camps: Boot camps will help you brush up your data science skills within 4-5 days. During the camp, you will be getting theoretical knowledge as well as practical hands-on experience.
  • MOOC courses: MOOC are the online courses where you can get assignments to work on your implementation skills. These also cover the latest industry trends.
  • Certifications: Certifications are a great way to test your skills and improve your CV.
  • Projects: Try taking on different projects that will help you implement your data science skills. Find different and better solutions to answer problems. This will improve your skills and thought process.
  • Competitions: You can try participating in online competitions like Kaggle where you have to find a solution to a problem with a given constraint.

According to Harvard Review 2012, data scientist is the sexiest job of the century. There are several organizations in Minneapolis, MN that are offering handsome pay to data scientists like Amazon Web Services, Risk Solutions, Virgin Pulse, Bind Benefits, Enable Data, TARGET, Ingersoll Rand, Cargill, Eaton, General Mills, UnitedHealth Group, Be the Match, US Bank, UMN, General Mills, Rich Products Corporation, Accenture, Blue Buffalo Co. Ltd Corporate, etc.

Practicing Data Science problems is the best way to become an expert in the Data Science field. Here, we have categorized some popular datasets categorized according to their difficulty level that you can use for practice.

  • Beginner Level
    • Iris Data Set: It is the easiest, most popular and versatile dataset that you can practice on as a beginner. It is a pattern recognition problem containing 4 rows and 50 columns. Practice Problem: You have to determine the class of the flowers.
    • Loan Prediction Data Set: This dataset will help you get an understanding of how the banking and insurance domains work. It is a classification problem with 16 columns and 615 rows.
      Practice Problem: You have to predict whether a certain loan will be approved by the bank or not.
  • Intermediate Level:
    • Black Friday Data Set: This dataset is collected from a retail store. This problem will include analyzing shopping habits of millions of customers. This regression problem with 12 columns and 550,069 rows will help you in expanding your engineering skills.
      Practice Problem: You have to predict the total amount of purchase made.
    • Human Activity Recognition Data Set: This dataset contains recordings from smartphones of 30 humans collected using inertial sensors. It has 561 columns and 10,299 rows.
      Practice Problem: You have to predict the category of human activity.
  • Advanced Level:
    • Identify the digits data set: This dataset contains 7000 images of 28X28 dimensions which you will be studying, recognizing, and analyzing.
      Practice Problem: You have to identify the different elements present in an image.
    • Vox Celebrity Data Set: With 100,000 words spoken by 1,251 celebrities, this dataset is used for large scale speaker identification. The words are collected from YouTube videos.
      Practice Problem: You have to identify the voice of a celebrity.

How to Become a Data Scientist in Minneapolis, Minnesota

If you want to become a top notch data scientist, you need to follow these steps:

  1. Getting started: You need to choose a programming language that you are comfortable working in. Python and R are the most commonly used languages in Data Science projects.
  2. Mathematics and statistics: To deal with numerical or textual data, you need to have a basic understanding of mathematics and statistics.
  3. Data visualization: Visualizing data in the form of graphs and charts makes the data easy to understand. It also promotes better communication with the end users.
  4. ML and Deep learning: Machine Learning and Deep learning skills are for data analysis and gathering information from it.

Data Scientist was termed as the ‘Sexiest job of the 21st Century’ by the Harvard Business Review in 2012. This has made the field attractive to so many developers. But, Data Science is a huge field which makes starting a career in it difficult. Here, we have compiled a list of steps that will help you acquire appropriate skills:

  • Certification: You can start with getting yourself enrolled for a course. This course can be either online or offline that covers the fundamentals of the Python programming language. A degree from a reputed institution or certifications in Data Science will help you start your career in the field of Data Science.
  • Unstructured data: Most of the data that is produced is in the raw form which means that is unlabelled and unorganized. To make this data ready for analysis, a data scientist must learn how to preprocess this data to structure it and make it useful.
  • Frameworks and Software: To become a Data scientist, you need to have an understanding of the software, frameworks, and programming languages used in Data Science projects.  
    • For statistical programs, R is the most preferred programming language.
    • Frameworks like Hadoop and Spark are used for handling the problems where the available memory is less than the amount of data.
    • Also, you will need to have an-in depth knowledge of databases and writing SQL queries.
  • Deep Learning and Machine Learning: Machine Learning and Deep Learning skills are required for analyzing the data and getting information from it.
  • Data Visualization: It is one of the most important skills required to be a data scientist. It is the job of a data scientist to visualize the data in the form of graphs and charts so that it is easy to understand. Tools like matplotlib, ggplot2, etc. are used for data visualization.

There are many accredited universities in Minnesota offering degrees related to data science. If you want to earn a degree in Data Science, you can try the Master’s program at the University of Minnesota, Syracuse University, Capella University, etc. Getting a degree in Data Science can help you get a jumpstart in your career. This is the reason why 88% of all Data Scientists have a Master’s degree while 46% of them also possess a PhD. The reasons why a degree is so important to get a job in the field of Data Science are:

  • Networking: When you will be pursuing your degree, you will get an opportunity to establish your network by creating friends and acquaintances. This network will help you land a better job in the future.
  • Structured education: During the course, you will have to follow a strict schedule and a curriculum. This will help you better learn the concepts of data science.
  • Internships: An internship is a part of the degree. This is very beneficial for you as this will provide you the required experience working on real-world projects.

You can decide if you need a Master’s degree or not by using the below-mentioned scorecard. If you score 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

Programming language is the most important skill required to become a data scientist. A data scientist has to deal with large datasets. For the analysis of the dataset, programming skills are required. Programming skills are required for building frameworks suitable for the organization. This framework must be able to analyze the experiments, perform data visualization, and manage the data pipeline automatically.

Data Scientist Jobs in Minneapolis, Minnesota

Getting started in the field of Data Science can be difficult. There is much to learn and so much to choose from. But don’t worry! Here, we have compiled a list of steps that will help you to get a job in Data Science:

  1. Getting started: The first step is to understand what your roles and responsibilities will be as a data scientist. Next, select the language you will be using for your data science projects. The most preferred programming languages used in Data Science are Python and R.
  2. Mathematics: You will need Mathematics and Statistics for deciphering patterns in the data. You need to cover important topics like linear algebra, probability, inferential, and descriptive statistics.
  3. Libraries: There are several libraries that are used for carrying out different processes in Data Science like Pandas, gglpt2, SciPy, Scikit-learn, matplotlib, NumPy, etc. They aid in preprocessing the data, plotting it and applying machine learning algorithms to it.
  4. Data visualization: You need to learn how to visualize the data in a form that is easy to understand. This includes graphs and charts. There are certain tools like ggplot2, matplotlib, etc. that can be used for data visualization.
  5. Data preprocessing: This step is required to make the raw data, available in the unstructured form, ready for analysis. Variable selection and feature engineering are required to perform this.
  6. ML and Deep learning: You will require machine learning and deep learning skills to analyze the data. Make sure that you have a thorough understanding of topics like RNN, CNN, Neural Networks, etc.
  7. Natural Language processing: For processing and classification of textual data, you need to have an understanding of Natural Language Processing.
  8. Polishing skills: Online competitions like Kaggle can help you implement and polish your data science skills. You can try creating your own projects or experimenting with others.

Here are the 5 important steps that you must follow to prepare for a job as a Data Scientist:

  1. Study: Study important topics before the interview like statistics, probability, statistical models, neural networks, machine learning, etc.
  2. Meetups and conferences: Try visiting conferences, meetups, tech talks, etc. to build your professional network and expand your connections.
  3. Competitions: You can test your data science skills by participating in online competitions like Kaggle.
  4. Referral: Referrals have become the primary source of interview. You need to keep your LinkedIn profile updated.
  5. Interview: Go for the interview. It might take a couple of interviews before you actually get a job. Study the questions you didn’t knew the answer to in the interview.

The main job of a data scientist is to analyze the data to find patterns in it and decipher information from it. Here are the major roles and responsibilities of a Data Scientist:

  • Gather the data and separate the data required for the analysis. This data will be mostly in an unstructured form.
  • Next step is the organization and analysis of data.
  • After that you need to create machine learning programs, techniques, and tools that can decipher patterns in the data and make sense out of it.
  • Last step is to predict future outcomes by performing statistical analysis.

The ‘Sexiest job of the 21st century’, Data Scientist comes with perks like handsome salaries, equity shares, etc. A data scientist can earn about $122,029 per year in Minneapolis, MN.

The data provided is usually huge and in an unstructured form which makes the job of deciphering patterns and finding relationships even more difficult. The more difficult a job is, the more potential it has for career growth. Here is the career path of a Data Scientist:

Business Intelligence Analyst: A Business Intelligence Analyst’s job is keeping a check on the latest trends and figuring out what is the best for business. They have a clear understanding of where their organization stands.

Data Mining Engineer: The role of a data mining engineer is examining the data. They are also responsible for creating algorithms that are required for data analysis. Many organizations hire data mining engineer as a third-party.

Data Architect: They work with developers, system designers, and users for creating blueprints for the data management systems required to integrate, centralize, protect and maintain the data source.

Data Scientist: It is the responsibility of a data scientist to perform the analysis, develop an understanding of data, explore patterns and create a hypothesis. After this, they are also required to develop algorithms and systems required to use this data for the benefit of the business.

Senior Data Scientist: A Senior Data Scientist is responsible for anticipating the needs of the business and shapes their data science projects accordingly. They also modify the data analysis system and process to meet the needs of the business.

Networking with other Data Scientists is very important. This will help you during the referrals. Here is how you can network with other data scientists:

  • Data Science conference
  • Online platforms like LinkedIn
  • Social gatherings like meetups

There are several career options for a data scientist –

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

A data scientist must have the mastery over the following to get a job in the field of data science:

  • Education: A data scientist must have either a Master’s degree or a PhD in Data Science. A couple of certifications in data science will improve your CV as well.
  • Programming: One of the most basic and important skills that you can have as a data scientist is programming. Python and R are the first choice of data scientists. After that, you can move on to data science libraries.
  • Machine Learning: To analyze patterns in the data and find relationships between them, you will require machine learning and deep learning skills.
  • Projects: Get some hands-on experience in data science skills by creating your own projects or exploring different solutions to old projects.

Data Science with Python Minneapolis, Minnesota

Python is considered to be one of the most popular programming languages in Data Science. It is because of the multiple features and advantages it offers over other programming languages:

  • Multi-paradigm programming language – This structured and object-oriented programming language offers several libraries and packages that ease the development process in Data Science.
  • Simple and readable – Python has an easy syntax, it is due to its readability and simplicity that it is so popular among data scientists. There are analytical libraries and packages dedicated to Data Science.
  • Wide range of resources – There are a number of resources available for python that can help a programmer out wherever he/she is stuck.
  • The Python community – Python is supported by its big, open source community that consists of millions of python developers working on thousands of different projects. So, whenever you are stuck at a problem, chances are that someone has been stuck there before and found a solution for it. Even if there is no solution, the developers in the python community will not back out from helping a fellow developer.

The 5 most popular programming languages used in the Data Science field include the following:

  • R: R is considered a difficult language to learn. However, due to the below-mentioned advantages, the language is one of the most preferred languages by data scientists:
    • High-quality open source packages created by the R community
    • Can handle complex matrix equations
    • Smoothly deals with statistical functions
    • Provides data visualization with gglplot2.
  • Python: Python is the most popular programming language used in data science due to the following:
    • Syntax that is easy to learn, read, understand, and implement.
    • A big open-source community.
    • Several libraries that can be used for data science projects like Pandas, scikit-learn, and tensorflow.
  • SQL: SQL or structured Query language is required for working with databases. It can be used to update, query or manipulate data present in the database. It has a pretty easy syntax to read and write.
  • Java: Even though it has limited verbosity and fewer libraries, it offers some advantages that makes it still relevant in the field of Data Science:
    • There are systems that have Java in their backend. So, if you want to integrate a data science project to it, you need to have the knowledge of Java.
    • It is a compiled, high-performance, and general purpose language.
  • Scala: Despite its difficult syntax, Scala is used in multiple data science projects because of the following:
    • It is compatible with Java as it runs on JVM.
    • When used with Apache Spark, it can help in getting high-performance cluster computing.

Here is how you can download and install Python 3 on Windows:

  • Download and setup: Go to the download page and set up python for your windows using the GUI installer. Select the checkbox that asks you to add Python 3.x to PATH. You can use the following command to check if python is installed in the system or not:

         python --version

  • For updating and installing libraries, type the following command:

python -m pip install -U pip

To download and install Python 3 on Mac OS X, you need to either download the .dmg package from their official website or use the Homebrew python. Here is what you need to do:

  • Install Xcode: You need to install the Xcode package of Apple/ Start by typing in the following command: 

$ xcode-select --install

  • Install brew: Next, start the installation of Homebrew with the following command: 

/usr/bin/ruby -e "$(curl -fsSL

https://raw.githubusercontent.com/Homebrew/install/master/install)" 

To confirm if it is installed, type the command: brew doctor

  • Install python 3: Use the following command to install Python: brew install python
    • Use the command to ensure that python is installed on the system: python --version

To run different python versions in different projects, you will have to create isolated places. 

This can be done by installing virtualenv.

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KnowledgeHut is a great platform for beginners as well as the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas and the concepts.

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Attended PMP® Certification workshop in May 2018
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My special thanks to the trainer for his dedication, learned many things from him. I would also thank for the support team for their patience. It is well-organised, great work Knowledgehut team!

Mirelle Takata

Network Systems Administrator
Attended Certified ScrumMaster®(CSM) workshop in May 2018
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I had enrolled for the course last week. I liked the way KnowledgeHut framed the course structure. The trainer was really helpful and completed the syllabus on time and also provided live examples which helped me to remember the concepts.

York Bollani

Computer Systems Analyst.
Attended Agile and Scrum workshop in May 2018
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KnowledgeHut has all the excellent instructors. The training session gave me a lot of exposure and various opportunities and helped me in growing my career. Trainer really was helpful and completed the syllabus covering each and every concepts with examples on time.

Felicio Kettenring

Computer Systems Analyst.
Attended PMP® Certification workshop in May 2018

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

Have More Questions?

Data Science with Python Certification Course in Minneapolis, MN

Minneapolis is quite the city of contradictions. Hostile weather but friendly locals, frozen winters but warm summers, rowdy rock clubs and sophisticated art museums. The city is known for its never say never attitude and this has put it on the road to success. Today its economy is being driven by technology, commerce, finance, rail, transport, and manufacturing industries. Several Fortune 500 companies including Accenture, Canadian Pacific, Target, Wells Fargo and others are headquartered here. All these offer excellent employment prospects earning Minneapolis the distinction of being one among the Seven Cool Cities for young professionals. Known for its vibrant art and culture scene, the place inspires many an artist and musician and brings out their creative best. KnowledgeHut offers several courses that help you start off your career in Minneapolis including, PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses, Big Data Analysis, Apache Hadoop, SAFe Practitioner, Agile User Stories, CASQ, CMMI-DEV and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.