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).
In 2012, the Harvard Review named Data Science as the sexiest job of the 21st century. What makes data science such a hot topic? The answer is simple, data. Data has become an integral part of our lives and it has become difficult to ignore its potential. In a city like Fremont, CA, there are several corporations that are looking for data scientists to help them harness the potential of data including Tailored Brands, Softsol, Astreya, WAY, Snapwiz, Facebook, Trabajo, SLD Laser, pony.ai, Soraa, Inceptio Technology, SSIT, Central Business Solutions, Quest Groups, Lam Research Corporation, etc. From classifying target audience to improving customer experience across devices and channels, data science offers immense value to businesses.
The top technical skills required to become a data scientist in Fremont, CA, USA, include the following:
To become a successful data scientist, one must have the following behavioral traits:
If you are not sure whether you should study data science or not, here are 5 benefits of becoming a data scientist that will help you decide:
If you want to become a top-notch data scientist, you need to have these 4 business skills:
If you are thinking of going for an interview to get a job in the field of data science, here are the 5 best ways to brush up your data science skills:
There are several companies that have understood the potential of data science and are actively looking for data scientists in Fremont, CA, including Tailored Brands, Softsol, Astreya, WAY, Snapwiz, Facebook, Trabajo, SLD Laser, pony.ai, Soraa, Inceptio Technology, SSIT, Central Business Solutions, Quest Groups, Lam Research Corporation, Harnham, Tesla, Seagate Technology, Sleep Number, Tokyo Electron America, Ivy Exec, etc. to help them optimize their business processes.
To practice your data science skills with data sets, you can select one of the following problems which are categorized based on your expertise and their difficulty level:
If you want to become a top-notch data scientist, you need to follow the below-mentioned steps:
Here are some key steps and skills that are a must if you want to become a successful data scientist:
A degree in data science can help you get a job and jumpstart your career. Here is how::
If you are having trouble deciding if you should get a Master's degree in Data Science or not, here is a scorecard that will help you do so. If your total adds up to more than 6 points, a degree is advised:
Programming language is one of the most important requirements to become a data scientist. Here are the reasons explaining why:
If you want to get a job as a data scientist, you need to follow the below-mentioned learning path:
If you are preparing for a job as a data scientist, here are 5 important steps that you must follow:
The job of a data scientist is complex. There are several roles and responsibilities of a data scientist including the following:
A data scientist is a part mathematician, computer scientist, and part trend spotter. Here is how the career path of a data scientist goes:
The top 8 data science career opportunities in 2019 are –
To get employed as a data scientist, you need to have mastery of the following tools and software:
Python is considered to be the most preferred and popular language in the field of data science. It is a structured and object-oriented language that offers several libraries and packages that help while working in data science projects. It is simple and easy to learn, read, and understand. There are also several resources available that will help you learn the language and find a way out whenever you are stuck in a problem.
The 5 most popular programming languages used in the field of data science include:
To download and install Python 3 on Windows, you need to follow these steps:
python -m pip install -U pip
To install Python 3 on Mac OS X, follow these steps:
$ Xcode-select --install
brew install python
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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