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.
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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 amount of data that is produced every day is just mind-boggling. By 2020, it's estimated that 1.7MB of data will be created every second for every person on earth. Data-driven decision making is increasing in demand. Data scientists help a company take those important marketing decisions based on data. San Diego, California is a hub for genetics and biotechnology. It is home to several leading companies such as Brain Corp, Patient Safe Solutions, Viasat, Illumina, Mitchell International, Inc. and all these companies are looking for skilled data scientists to analyze their data.
Below are the behavioral traits employers look for in a Data Scientist in San Diego, CA -
Expect to enjoy the following benefits on the job:
Below is a list of the business skills you need to become a data scientist:
Below are the best ways to brush up your data science skills for data scientist jobs:
San Diego, California is home to many leading companies, such as Brain Corp, Patient Safe Solutions, Viasat, Illumina, Mitchell International, Inc. and many more. It is a fact that every company needs data. The data science job offered by companies are determined by what kind of companies they are. Small companies use Google Analytics for their analysis - as they have fewer resources and fewer data to work with. Mid-size companies need someone to apply ML techniques on it to leverage it. Big companies already have teams of data scientists, so they would be needing a new data scientist with specialization. For eg: Visualization, ML expert etc.
We have compiled a list of data sets you can practice on, categorized according to their difficulty level for your ease:
elow are the steps to becoming a successful data scientist in the city of San Diego:
Here are some key steps and skills that will help you become a successful data scientist:
A degree is very important in the field of Data Science because of the following –
Below is a scorecard to help you in making this decision.If your total adds up to more than 6 points, it would be advisable for you to earn a Master’s degree.
Here are the reasons why knowledge of programming language is a must:
Here is the logical sequence of steps you should follow to get a job as a Data Scientist in San Diego, CA.
If you are thinking to apply for a data science job in San Diego, CA, then follow the below steps to improve your chances:
A data scientist is an individual who is responsible for discovering patterns and inferencing information from vast amounts of structured as well as unstructured data, in order to meet the business goals and needs.
Major Data Scientist Roles & Responsibilities:
A career path in the field of Data Science can be explained in the following ways:
Business Intelligence Analyst: A Business Intelligence Analyst does the analysis of data in order to figure out how the business works and how it can be affected by market trends.
Data Mining Engineer: A Data Mining Engineer is an individual who has the job of examining the data for the needs of the business. He needs to create sophisticated algorithms that further aid in the analysis of data.
Data Architect: The role of Data Architect is to work with database administrators and analysts to secure easy access to company data.
Data Scientist: The main responsibility of a Data Scientist is to pursue a business case by analysis, development of hypotheses as well as the development of an understanding of data, so as to explore patterns from the given data.
Senior Data Scientist: A Senior Data Scientist is tasked with the anticipation of business needs in the future and shaping the projects, systems and data analyses of today to suit those business needs in the future.
San Diego, California is growing as one of the hubs of Data Science in USA. Some of the ways to network with fellow data scientists are:
There are so many career options for a data scientist in San Diego, California as of today-
Here are the tools and software that a data scientist must be an expert in:
The simplicity and readability of Python makes it popular among data scientists. It is a multi-paradigm programming language and comes with a broad and diverse range of resources that are available to the data scientist. Python is supported by its big, open-source community. With many developers working on Python every day, it becomes very easy for a developer to get help in resolving his/her problems.
Below are the most commonly used programming languages in Data Science:
These are the steps to install Python 3 on windows:
python -m pip install -U pip
To install python 3 on Mac OS X, follow the below steps:
$ xcode-select --install
See if it installed correctly: brew doctor
brew install python
Check if it’s installed by typing: python --version
Note: Install virtualenv, as it will help you create isolated places to run different projects and may run even on different python versions.
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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