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.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
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.
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.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
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.
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.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.
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.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
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).
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:
The top 5 essential behavioral traits of a successful data science professional:
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:
Some of the business skills that are needed to become a data scientist are:
The 5 best ways to brush up your Data Science skills to get a job as a Data Scientist are:
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.
If you want to become a top notch data scientist, you need to follow these steps:
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:
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:
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:
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:
Here are the 5 important steps that you must follow to prepare for a job as a Data Scientist:
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:
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:
There are several career options for a data scientist –
A data scientist must have the mastery over the following to get a job in the field of data science:
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:
The 5 most popular programming languages used in the Data Science field include the following:
Here is how you can download and install Python 3 on Windows:
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:
$ xcode-select --install
/usr/bin/ruby -e "$(curl -fsSL
To confirm if it is installed, type the command: brew doctor
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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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 for me.
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This is a great course to invest in. The trainers are experienced, conduct the sessions with enthusiasm and ensure that participants are well prepared for the industry. I would like to thank my trainer for his guidance.
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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.
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
There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
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.
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.
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
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.