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

Get hands-on Python skills and accelerate your data science career

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit.
  • Create robust predictive models with advanced statistics.
  • Leverage hypothesis testing and inferential statistics for sound decision-making.
  • 250,000 + Professionals Trained
  • 55,000 + Programmers upskilled
  • 70 + Countries and counting

Grow your Data Science skills

This four-week course takes you from the fundamentals of Data Science to an advanced level. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

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Highlights

  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to Advanced Learning

  • Code Reviews by Professionals

Why Become a Data Scientist?

Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need.

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The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Exclusive Post-Training Sessions

Practical one-to-one guidance from mentors: project review and evaluation, guidance on work challenges.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Prerequisites

Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course.
  • Elementary programming knowledge will be useful.

Who should attend this course?

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 researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

1

Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

2

User-defined functions in Python

Lambda function and the object-oriented way of writing classes and objects.

3

Datasets and manipulation

Importing datasets into Python, writing outputs and data analysis using Pandas library.

4

Probability and Statistics

Data values, data distribution, conditional probability, and hypothesis testing.

5

Advanced Statistics

Analysis of variance, linear regression, model building, dimensionality reduction techniques.

6

Predictive Modelling

Evaluation of model parameters, model performance, and classification problems.

7

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Learning objectives

Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.


Topics

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

Learning objectives

The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization

Topics

  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study

Hands-on

  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives

In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value

Topics

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

Hands-on

  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives

Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.

Topics

  • Analysis of Variance (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 for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives

Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression & classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study

Topics

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

Hands-on

  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives

All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

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

Topics

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

Hands-on

  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing 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.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

Learning objectives

This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.


Hands-on

  • Project to be selected by candidates.

Frequently Asked Questions

Data Science with Python Training

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.

Data Science with Python Workshop

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.

What Learners Are Saying

Ong Chu Feng

Ong Chu Feng

Data Analyst

4/5

The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to un View More

Attended Data Science with Python Certification workshop in January 2020

Rosabelle Artuso

Rosabelle Artuso

.NET Developer

5/5

The course which I took from Knowledgehut was very useful and helped me to achieve my goal. The course was designed with advanced concepts and the tasks during the course given by the trainer helped me to step View More

Attended PMP® Certification workshop in August 2020

Jules Furno

Jules Furno

Cloud Software and Network Engineer

5/5

Everything from the course structure to the trainer and training venue was excellent. The curriculum was extensive and gave me a full understanding of the topic. This training has been a very good investment fo View More

Attended Certified ScrumMaster (CSM)® workshop in June 2020

Astrid Corduas

Astrid Corduas

Senior Web Administrator

5/5

The skills I gained from KnowledgeHut's training session has helped me become a better manager. I learned not just technical skills but even people skills. I must say the course helped in my overall development View More

Attended PMP® Certification workshop in April 2020

Astrid Corduas

Astrid Corduas

Telecommunications Specialist

5/5

The instructor was very knowledgeable, the course was structured very well. I would like to sincerely thank the customer support team for extending their support at every step. They were always ready to help an View More

Attended Agile and Scrum workshop in June 2020

Estelle Dowling

Estelle Dowling

Computer Network Architect.

5/5

I was impressed by the way the trainer explained advanced concepts so well with examples. Everything was well organized. The customer support was very interactive.

Attended Agile and Scrum workshop in February 2020

Rafaello Heiland

Rafaello Heiland

Prinicipal Consultant

5/5

I am really happy with the trainer because the training session went beyond my expectations. Trainer has got in-depth knowledge and excellent communication skills. This training has actually prepared me for my View More

Attended Agile and Scrum workshop in April 2020

Anabel Bavaro

Anabel Bavaro

Senior Engineer

5/5

The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut. I really liked the way the trainer explained the concepts. He was very patient and well informed.

Attended Certified ScrumMaster (CSM)® workshop in August 2020

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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.

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.

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.

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.

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