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

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

• Build Python programs: distribution, user-defined functions, importing datasets and more
• Manipulate and analyse data using Pandas library
• Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
• Distribution of data: variance, standard deviation, interquartile range
• Calculating conditional probability via Hypothesis Testing
• Analysis of Variance (ANOVA)
• Building linear regression models, evaluating model parameters, and measuring performance metrics
• Using Dimensionality Reduction Technique
• Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
• Building KNN algorithm models to find the optimum value of K
• Building Decision Tree models for both regression and classification problems
• Build Python programs: distribution, user-defined functions, importing datasets and more
• Manipulate and analyse data using Pandas library
• Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
• Build data distribution models: variance, standard deviation, interquartile range
• Calculate conditional probability via Hypothesis Testing
• Perform analysis of variance (ANOVA)
• Build linear regression models, evaluate model parameters, and measure performance metrics
• Use Dimensionality Reduction
• Build Logistic Regression models, evaluate model parameters, and measure performance metrics
• Perform K-means Clustering and Hierarchical Clustering
• Build KNN algorithm models to find the optimum value of K
• Build Decision Tree models for both regression and classification problems
• Build data visualization models for Time Series data and components
• Perform exponential smoothing

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

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

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

• Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
• 4 GB RAM
• 10 GB of free space

Software Requirements

• Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

• 32 or 64-bit Operating System
• 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most 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 immediately applicable skills to real-world problems.

Such a 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 online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

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

Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

With our 100% Satisfaction Guarantee policy, you are assured of the highest standards of quality. At KnowledgeHut, we strive to ensure that your learning experience with us is beyond reproach. However, if for any reason your expectations are not met, we will refund the course fee in accordance with our 100% Satisfaction Guarantee policy.

Yes, you can switch the start date to your Data Science with Python training, with prior notice of at least 24 hrs and subject to availability in the desired batch.

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

Yes, scholarships are available for students and veterans with grants up to 50% of the course fees.

To avail scholarships for our Data Science with Python course, please get in touch with us at support@knowledgehut.com. The team shall send across the forms and instructions 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. By registering in groups, you can typically save up 20% to 30% on the course fee. For more details, please check out upcoming schedules.

Yes, instalment options are available for payment of course fee. To avail the instalment option, please get in touch with us at support@knowledgehut.com. The team will explain how the instalments work and provide timelines for your case.

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

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.

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.

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.

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.

Following are the top professional organizations for data scientists in Minneapolis:

• Social Data Science
• Python Data Science
• Data Science Minneapolis
• She talks Data
• Minneapolis Women in Machine Learning and Data Science
• Futurist Academy

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.

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.