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Data Science with Python Training in Irvine, CA, United States

Learn to analyze data with Python in this Data Science with Python comprehensive course.

  • 42 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms

Online Classroom (Weekday)

Mar 29 - Apr 26 04:00 PM - 06:00 PM ( PDT )

USD 2199

USD 649

Online Classroom (Weekday)

Mar 29 - Apr 26 05:00 PM - 07:00 PM ( PDT )

USD 2199

USD 649

CITREP+ funding support is eligible for Singapore Citizens and Permanent Residents


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 analyzing, 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 interview prep will help you land lucrative jobs.

What You Will Learn


There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

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

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.


Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

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

Hands-on:  No hands-on

Learning Objectives:

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.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study


  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

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.

Topics Covered:

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


Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

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.

Topics Covered:

  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA


  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

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.

Topics Covered:

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


  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.

  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

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

Topics Covered:

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


  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write 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.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

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.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor


 Project to be selected by candidates.


Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

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).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science

Investments in the digital enterprise have been drastically increased over the past few years. It has been estimated that by 2020, the IT field will be monitoring 50 times more data than it is today. An interdisciplinary field, data science deals with processes and systems that are used to extract knowledge from large amounts of data. 

Known as the ‘city of innovation’, Irvine has become a big tech hub and home to numerous companies, start-ups and innovative people. Home to several leading companies, Irvine is a great place to kickstart your Data Science career because of the multiple opportunities of growth in the city. Some of the corporations offering jobs to Data Scientists include Amazon Web Services, Ten-X, UST Global, Karma Automotive, BookingPal, Allergan, etc. 

Looking for a job in data science at Irvine, CA? Here are five reasons why it’s the best career choice for you:

  1. Data science helps companies understand their customers better. They can connect with them in a personalized manner, thus helping in better brand power.
  2. It is a new field that’s constantly growing. With the increase in the number of tools, it’s helping organizations solve complex IT and resource management problems in a strategic manner. This implies effective use of resources.
  3. The location of the organization is advantageous as Irvine is filled with innovative companies and this kind of environment promotes team work, collaboration and tight-knit environment.
  4. The results of data science can be applied to almost any sector like travel, healthcare, education etc. Irvine has been flourishing in the fields of healthcare and education, thus allowing data scientists to help analyse their challenges and tackle them effectively.
  5. Organizations in Irvine are looking out for fresh graduates due to their high qualification in data science, making it one of the highest paying jobs.

All these factors teamed up with the need for proper utilization of data will hold the key for achieving the set targets for both companies and individuals.

We have developed from complete randomness to finding patterns, predictions to calculations etc. The enablers of such drastic change, that is, the data scientists keep discovering solutions to the most complex problems, create patterns and filter them to give the best results. The University of California located in Irvine offers a Data Science Certificate, Business Intelligence & Data Warehousing Certificate, Predictive Analytics Certificate Program, and Master of Science in Business Analytics. While curiosity is the major catalyst for a career choice like this, there are many technical skills that one must possess in order to not just get hired, but also to thrive.

  • C/C++ and Java Coding: These are common coding languages that form the basis of any technical job.
  • Python coding: Its versatility as a coding language allows the smooth functioning of almost all steps in data science. Data scientists have to deal with huge amounts of data (big data). Due to its simplicity of use and a large set of python libraries, it has become the preferred language to handle big data. Also, Python can be easily integrated with other programming languages. The applications built using Python are easily scalable and future-oriented.
  • R Programming: R is a language that is used primarily for data analysis. Knowing how to use R is important because it helps in cleaning complex data sets to ensure convenience and further analysis. It has an extensive library of tools for database manipulation and also makes Machine Learning easier.
  • SQL Database/Coding: SQL is necessary to communicate with the database so as to work with the data. SQL also integrates with the scripting languages and you can connect a client app to your database.
  • Apache Spark: The primary importance of Spark in the Big data industry is because of its in-memory data processing that makes it a high-speed data processing engine. It delivers a better-integrated framework which supports all ranges of Big data formats. Apache Spark allows programmers to write applications using Python, Clojure and Java as it is supported by over 80 high-level operators. 
  • Machine learning and AI: Machine learning techniques like neural networks, reinforcement learning etc. are techniques that data scientists mu7st be well versed in. If you want to stand out, you should know techniques such as supervised machine learning, decision trees etc. These are important in solving different data science problems that are primarily based on the predictions of major outcomes.
  • Data visualization: Data should be in a form that is easy to understand. As a data scientist, it is important for you to visualize data. This can be done using data visualization tools like ggplot, d3.js and Matplottlib. Data visualization gives organizations the opportunity to work with data directly in order to act on future prospects.
  • Understanding of structured data: Unstructured data is the content that isn’t a part of data tables, for example, videos, customer reviews, blog posts etc. Sorting these types of data is difficult. Working with unstructured data helps you to gain a broader perspective for decision making.

What do you think makes a good data scientist? Having the essential technical skills is very important, there are some traits that your employers look for during the hiring process.

  • Data intuition: This is the quality that helps one to identify patterns within sets of structured and unstructured data. The role of data scientists is constantly evolving and they must now understand the needs of customers and their organization. For your interview, you may be asked to create quick data visualization. 
  • Passion: Data Science is not just a science, it is also an art. In a world filled with mediocrity, one must come up with the best solution to a complex problem. A data scientist has to keep pushing to find the solution that will optimize business value. Without passion for the field of study, a data scientist will not be able to find that optimal solution.
  • Curiosity: Data Science is a field in which innovations are being done every year. This is because the best data scientists are always looking for alternative ways to solve problems. This includes searching for new and optimal ways to acquire and merge data, pre-process features, or develop models and improve their run time using a combination of software and hardware optimizations. For this, curiosity is a key factor in determining the extent of innovation.
  • Strong will: Sometimes, problems might be so complex that you might quit trying to find solutions. To be a data scientist, you must possess the ability to get back up every time you fall. Patience and the will to come up with optimal solutions are necessary so as to show your professionalism.
  • Ability to make data-driven decisions: A data scientist cannot conclude, judge or decide without adequate data. Scientists need to decide their approach to a business problem in addition to deciding several other things like where to look, what tools to use and how to visualize and communicate it in the most effective way. 

The combination of technical skills and these characteristics is what differentiates a great data scientist from a mediocre one.

As the field of data science is becoming more popular, plenty of job opportunities have opened up in this field. Some of the leading companies offering employment to Data Scientists include Capital Group, Alteryx, Inc., BlackBerry, Edwards Lifesciences, Drybar,, KPMG, Synaptics, Prismatik, etc. The benefits of being a data scientist are:

  • Freedom to work: Any data scientist’s answer to what they like best about their field will be freedom. You won’t be bound to work for a particular industry. You can explore the tech world by working on projects that interest you and you can change lives.
  • Handsome pay: Most of the graduates, especially those in Irvine, have been earning close to $120,000. Thus job holds the highest position among the best jobs, making it such a viable option.
  • Chance to work with big brands: All big companies are on the lookout for data scientists for marketing and selling their products. If you want a chance to work with the big players like Amazon, Facebook, Google, Apple etc., this is the right career for you.
  • Secure career: While people argue that technology is temporary and will wear out, the same doesn’t hold true for data science. It will breed further and the need for scientists will keep growing. Your skills and attitude will keep you for a long time on this field.
  • Building your own business becomes easy: Once you have understood the working of various industries, your relationship with the clients is great and you learn how to solve real problems. Since you’ll have field experience, data science will aid you to set up your own dream business.

Data Scientist Skills & Qualifications

Apart from technical skills, a data scientist has to possess skills that can help in finding solutions for real-industry problems.

  • Statistical thinking: Hypothesis testing, Probability, Descriptive and Inferential Statistics are building blocks for data science. A data scientist should be able to interpret statistical output in business friendly context. Understanding the right algorithms and programming can make you a major player in this field.
  • Good communication skills: You should be able to present analytical solutions in a clear and concise manner. To translate statistical output into recommendations for action and to be able to discern team perception requires you to have excellent verbal and written communication skills.
  • Problem solving ability: Companies look for those who can think out of the box and who are comfortable in creatively solving business related problems. This is the most important skill for a data scientist.
  • Teamwork: Although data scientists are treated individually for their abilities, it is crucial that they work together in a team because it is a sign of all skills aligning on the same page. More creative ideas come up, leading to improved solutions.

Being an all-rounder is an empowering feeling. Here are the five best ways to brush up your skills to become a data scientist:

  • Participate in competitions: When you need to get back into coding, the most difficult thing to do is to think about what problem to solve. There exist a multitude of platforms built for coders. Getting into a coding competition, or a ‘hackathon’, is one of the best ways to brush up your skills after a long period of not coding. You might also learn new methods to code.
  • Be open to learning more: Take online courses from Coursera or any other platform that focus on developing your hold over a particular language or skill. There are many interactive incentive-based platforms too.
  • Freelance and contribute to open source projects: Free and open source software is at the root of many programming languages and of some of the most ambitious projects in the world. You can code in the language of your choice and thus, brush up your skills.
  • Go through professional code samples: Clean coding is an important skill to have, as it greatly increases readability and debug capabilities. One of the best ways to create clean code is to take a look at the code samples which are created by some of the best coders around.
  • Take help from a mentor: Find a mentor who works in the industry or has experience in data science. This includes programmers and developers, data scientists, statisticians, engineers and more. Ask them questions and learn about their experiences.

There is a data boom all around the world today. From travel to education to healthcare- data analysis has become extremely crucial. The ‘city of innovation’, Irvine, is flourishing in the travel and healthcare industries. The companies look for different data scientists for different purposes. In Irvine, all corporations including small-sized companies to bir corporations, everyone is looking for data scientists for providing useful insights and helping in making crucial marketing decisions from data to optimize their business. The companies that are currently employing data scientists include Capital Group, Alteryx, Inc., BlackBerry, Edwards Lifesciences, Drybar,, KPMG, Synaptics, Prismatik, Amazon Web Services, Ten-X, UST Global, Karma Automotive, BookingPal, Allergan, etc.

For any goal that one wants to achieve, practicing and working hard are the most important. Find what motivates you to practice what you’ve learned and learn more. This includes personal projects, competitions, online courses, reading research papers or meeting up with experts. To help you decide what kind of data sets you can work with, here are three levels:

Beginner level: This level has data sets that are easy to work with and don’t require complex techniques. They can be solved using basic algorithms. 

  • Heights and Weights Data Set: This is a fairly straightforward problem and is ideal for people who are starting data science. It is a regression problem where there are 25,000 rows and 3 columns (index, height and weight).PROBLEM: Predict the height or weight of a person. 
  • Time Series Analysis Data Set: It is one of the most commonly used techniques in data science. It is used in weather forecasting, predicting sales etc.PROBLEM: Predict the traffic on a new mode of transport.
  • Loan Prediction Data Set: The banking domain has the greatest use of data science methodologies as compared to any other industry. The Loan Prediction data set provides the learner with a taste of working with the concept involved in banking and insurance - the challenges faced, the strategies implemented, the variables that influence the outcomes etc. The Loan prediction data set consists of 13 columns and 615 rows and is a classification problem data set.PROBLEM: Predict if a given loan will be approved by the bank or not.

Intermediate Level: This level has more challenging data sets. It consists of mid and large data sets which require serious pattern recognition skills.

  • Human Activity Recognition Data Set: The Human Activity Data Set has a collection of 30 human subjects that were collected via recordings by smartphones. These were embedded with inertial sensors. The Human Activity Recognition Data Set consists of 561 columns and 10,299 rows.PROBLEM: Predict the human activity.
  • Trip History Data Set: This comes from a bike sharing service in the US. It requires you to exercise your pro data munging skills. The data is provided quarter wise from 2010 and has 7 columns.
    PROBLEM: Predict the class of the user.
  • Million Songs Data Set: Data science is applicable in the entertainment industry too. This data set is a regression task. It has 515,345 observations and 90 variables.
    PROBLEM: Predict the release year of a song. 

Advanced Level: After getting a hold on the basics, this level will be perfect for people who understand neural networks, deep learning, recommended systems etc. It allows you to get creative.

  • Vox Celebrity Data Set: Audio processing is a challenging problem. This data set is for speaker identification and contains words spoken by celebrities that have been extracted from YouTube. It contains 100,000 words spoken by 1,251 celebrities.
    PROBLEM: Figure out which celebrity this voice belongs to.
  • Recommendation Engine Data Set: You will be given the data of the programmers and questions they have solved, along with the time they took to solve a particular question. The model you build will allow judges to decide the next level of questions to be recommended to the user.
    PROBLEM: Predict the time taken to solve a problem using the current status of the user.

How to Become a Data Scientist in Irvine, California

Ranked as the hottest job on offer in the coming years and coupled with handsome pay-checks, data science has become a top career choice. What will give you the competitive edge? Find out through these steps:

  • Develop skills in Algebra, Statistics and Machine Learning. The perfect balance will make you a top-notch data scientist.
  • Learn to embrace big data. Data scientists deal with large amounts of segregated and unsegregated data which requires big data software and the right skillset. 
  • Learn to code. This is the first and foremost requirement for a data scientist because you cannot deal with data if you don’t know the language in which the data communicates. A great data scientist is always a great coder.
  • Master data munging and visualization. It is important for a data scientist to know how to convert data into a form that is easy to study, analyse and visualize. 
  • Stay updated within the data scientist community. You should remain in sync with what is happening in the world of data science and the types of job openings offered in the field.

Step 1: Preparation

You can start preparation even before starting your college. Learn Python, Java, and R and rebuild your knowledge in applied math and statistics.

Step 2: Enrol for suitable courses

Try to get enrolled for courses such as data science, mathematics, information technology, computer science, etc. Continue to learn programming languages, database architecture and SQL/MySQL. 

Step 3: Get an entry-level job

Companies are often eager to fill entry-level data science jobs. Look for positions such as Junior Data Analyst or Junior Data Scientist. 

Step 4: Get a Master’s Degree and/or a Ph.D.

Data science is a field where career opportunities are higher for those with advanced degrees. So, get enrolled for master’s or Ph.D.

Step 5: Never Stop Learning

Staying relevant is crucial to the evolving field of data science. Continue to network and learn through boot camps and conferences.

Most of the data scientists today either hold a Master’s degree or a PhD. While possessing the required skills is the most important requirement to be a data scientist, the degree has statistically proven to be important in landing a job. The University of California located in Irvine offers a Data Science Certificate, Business Intelligence & Data Warehousing Certificate, Predictive Analytics Certificate Program, and Master of Science in Business Analytics. Having a degree has the following advantages:

  • Network: Building professional networks within your college communities kickstarts your career as you can showcase your skills and win competitions.
  • Internships: While pursuing your degree, you can easily bag internships in the field of data science which will not just add to your experience, but also help you develop your skills.
  • Jobs: Most of the jobs will demand an academic qualification in your field which is why a degree plays an important role.
  • Discipline: Studying and following a schedule allows you to develop the discipline which you will carry forward when you become a data scientist.

Candidates having a Master’s/PhD degree may have advantages because they may be able to do some or all of these below:

  • Do research involving programming and large datasets
  • Develop statistical and data intuition 
  • Answer hard questions
  • Critically think about hard problems

The University of California located in Irvine offers a Data Science Certificate, Business Intelligence & Data Warehousing Certificate, Predictive Analytics Certificate Program, and Master of Science in Business Analytics. However, a graduate with field experience need not pursue a Master’s if he is already working. Real experience will always outweigh the Master’s degree.

The demand for data scientists is growing in every industry. To become a data scientist, you require the right tools and skillset to produce better results. Data science deals with humongous amounts of data that these scientists need to work on. This data can be segregated or unsegregated, depending on its type. In a situation like this, the basic requirement would be to understand the language in which data communicates. These languages include Python, R, Java, etc. 

  • This knowledge allows statisticians to perform the most complex analyses without much difficulty.
  • These languages have multiple libraries to perform multiple roles.
  • They help retrieve data from organized data sources.
  • They can be used in conjunction with big data platforms.

Data Scientist Jobs in Irvine, California

Broadly, the learning path to become a data scientist can be divided into the following steps:

  1. Getting Started: The biggest step is the beginning of your data science journey. This stage is all about understanding what data science is and what a data scientist role entails. This is where you should pick up the programming language and tool of your choice. 
  2. Maths and Statistics: These are the core concepts a data scientist must know. Where learning a tool will help you perform quick calculations and generate results, you can’t truly become a data scientist until you have a solid grasp on statistical methods (probability, descriptive and inferential stats) and mathematical fields (linear algebra).
  3. Learning Machine Learning concepts: You should start learning the basics of machine learning. But this isn’t just limited to theoretical concepts. You should apply them too. But ML isn’t limited to just the algorithms; you need to know nifty tricks to improve your models.
  4. Introduction to Deep Learning: Now you know these machine learning concepts, what comes next? Deep learning of course! It’s becoming an essential part of any data scientist’s CV these days. Follow that up with a deep dive into advanced neural network frameworks, namely recurrent neural networks and convolutional neural networks. 
  5. Natural Language Processing (NLP): No data scientist learning path is fully complete without first going over NLP. You should focus on learning the basics at the very least, including text pre-processing and text classification.
  • Find the role you want. Data scientists deal with different problems in different companies. You should decide whether you want to be into analytics, algorithms or inference.
  • Study Statistics, Machine Learning, SQL and Python. It is important that you learn or brush up your skills when it comes to these as they lay the foundation of data science and all interview questions are based on these topics.
  • Study about the company culture, people and business models. If you apply for a job in a company, you should know the workings of all the models, the kind of problems you’ll be finding solutions for and the overall culture around.
  • Be Persistent. Getting a job means dealing with rejection. You should know how to be strong and apply smartly using your connections for jobs.
  • Negotiate and leverage. Keep a track of the current salary for data scientists and the particular role that you want to take up. Express your expectations politely and learn how to negotiate and create leverage.
  • Data scientists help companies interpret and manage data and solve complex problems using expertise in a variety of data niches. They generally have a foundation in computer science, modelling, statistics, analytics, and math -coupled with a strong business sense.
  • A Data Scientist identifies the data the business should be collecting, develops methods of instrumenting the system in order to extract this information and work with other departments to devise the processes that transform raw data into actionable ones.
  • They are responsible for determining the correct data sets and variables and collecting large sets of structured and unstructured data from disparate sources.
  • They clean and validate the data to ensure accuracy, completeness and uniformity.
  • It’s this merging of esoteric intelligence and practical knowledge that makes the data scientist so valuable to a company.

Data scientists are earning much more as compared to other jobs, especially in the US. Irvine is known for all its start-ups. The start-ups pay the highest, other companies pay lesser and public institutions pay the least. According to the roles:

  • Data Scientist- $120,179 p.a
  • Data Analyst- $71, 274 p.a
  • Senior Data Scientist- $140,000 p.a

The ability to manipulate and understand data is extremely critical in innovation. As a result, we are witnessing data science as a field that focuses on the processes and systems that enable us to extract knowledge and transform them into action. But as a discipline, it is in an infancy stage. All tech companies are driven towards data and hence, this is becoming a career with a lot of diversity. The career path in detail is as follows:

  1. Data scientistsHe/she will be able to create predictive models, discuss the findings after understanding the business problems. The role entails solving a data science problem after applying their theoretical knowledge of statistics and algorithms. 
  2. Data engineersThey use their software engineering experience to handle large amounts of data. They usually focus on coding, cleaning up data sets, and implementing requests that come from data scientists.
  3. Data architectsThey focus on structuring the technology that manages data models.
  4. Data administrators They focus on managing data storage solutions and fall in the category of data engineers.
  5. Data analystsData analysts look through the data and provide reports and visualizations to explain what insights are hidden in the data. 

The top professional associations and groups in Irvine for Data Scientists include:

  • OC Data Science
  • Fullerton Data Science and Artificial Community
  • Data Driven Insights
  • UIUC-MCS Data Science
  • Cerritos Data Visulaization BI Meetup

Connections are the best option to network with data scientists in Irvine. This can be done through:

  • Conferences
  • LinkedIn groups
  • Boot camps
  • MeetUp and other social gatherings
  • Data Scientist
  • Business Analyst
  • Business Intelligence Developer
  • Data Engineer
  • Machine Learning Engineer
  • Data Analyst
  • Data Architect

They look for:

  • Basic programming languages like Python and R.
  • Statistics (Hypothesis, probability etc.)
  • Machine learning 
  • Data wrangling(cleaning up data)
  • Data visualization

Data Science with Python Irvine, California

Python is a structured and object-oriented programming language that contains several libraries and packages that are useful for the purposes of Data Science. The inherent simplicity and readability of Python as a programming language makes it a language that is preferred by data scientists. Another great thing about Python which makes it the language of choice for data scientists is the broad and diverse range of resources that are available.

R Programming: R is one of the most frequently used programming tools for data science. It allows users to compute huge data sets, get statistical insights, create custom graphics and more. 

Python: Python is a very popular, dynamic and versatile data tool for analyzing, arranging and integrating data into complicated data sets and creating advanced algorithms. It is among the easiest programming languages and hence the most sought after platform by most data scientists.  

SQL: It is used for editing, customizing and arranging information in relational databases.

Java: Java is an extreamely compatible and comprehensive platform which runs on OOPS framework and hence is easy to customize.

The Python download requires about 25 MB of disk space; keep it on your machine, in case you need to re-install Python. When installed, Python requires about an additional 90 MB of disk space.

  • Click Python Download.

The following page will appear in your browser

  • Click the Download Python 3.7.0 button.

The file named python-3.7.0.exe should start downloading into your standard download folder. This file is about 30 MB so it might take a while to download fully if you are on a slow internet connection.

The file should appear as:

 Move this file to a more permanent location, so that you can install Python.

  • If you want to just continue the installation, you can terminate the tab browsing this webpage.
  • Start the Installing instructions directly below.
  • Double-click the icon labelling the file python-3.7.0.exe.

An Open File - Security Warning pop-up window will appear.

  • . Click Run.

A Python 3.7.0 (32-bit) Setup pop-up window will appear.

Ensure that the Install launcher for all users (recommended) and the Add Python 3.7 to PATH checkboxes at the bottom are checked.

If the Python Installer finds an earlier version of Python installed on your computer, the Install Now message may instead appear as Upgrade Now (and the checkboxes will not appear).

  • Highlight the Install Now (or Upgrade Now) message, and then click it.

A User Account Control pop-up window will appear, posing the question Do you want to allow the following program to make changes to this computer?

  • Click the Yes button

A new Python 3.7.0 (32-bit) Setup pop-up window will appear with a Setup Progress message and a progress bar.

During installation, it will show the various components it is installing and move the progress bar towards completion. Soon, a new Python 3.7.0 (32-bit) Setup pop-up window will appear with a Setup was successfully message.

  • Click the Close button.

  • Next, run the Python Installer to install Python 3 onto your Mac.
  • Now once Python 3 is installed, you’ll be able to find it within the Applications directory of your Mac. You’ll also find here a simple IDE called “” which gives you a basic Python IDE.

If you can’t find the Applications directory, simply go to Finder by clicking the Finder icon in the Dock (it’s usually the first icon from the left side of the Dock). From there simply, go to the Go menu and select Applications. 

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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 understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, KnowledgeHut!

Ong Chu Feng

Data Analyst
Attended Data Science with Python Certification workshop in January 2020
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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 up in my career. I loved the way the technical and sales team handled everything. The course I took is worth the money.

Rosabelle Artuso

.NET Developer
Attended PMP® Certification workshop in May 2018
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My special thanks to the trainer for his dedication and patience. I learned many things from him. I would also thank the support team for their help. It was well-organised, great work Knowledgehut team!

Mirelle Takata

Network Systems Administrator
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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I would like to thank the KnowledgeHut team for the overall experience. My trainer was fantastic. Trainers at KnowledgeHut are well experienced and really helpful. They completed the syllabus on time, and also helped me with real world examples.

Elyssa Taber

IT Manager.
Attended Agile and Scrum workshop in May 2018
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I am glad to have attended KnowledgeHut's training program. Really I should thank my friend for referring me here. I was impressed with the trainer who explained advanced concepts thoroughly and with relevant examples. Everything was well organized. I would definitely refer some of their courses to my peers as well.

Rubetta Pai

Front End Developer
Attended PMP® Certification workshop in May 2018
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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. Thank you KnowledgeHut.

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Senior Web Administrator
Attended PMP® Certification workshop in May 2018
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The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

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Senior Network System Administrator
Attended Agile and Scrum workshop in May 2018
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I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and I liked his practical way of teaching. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

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Web Developer.
Attended Certified ScrumMaster (CSM)® workshop in May 2018


The Course

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. 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.

  • Get advanced knowledge of data science and how to use them in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Learn tools like Python

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

  • Python
  • MS Excel

There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

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. 

Finance Related

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.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

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

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Data Science with Python Certification Course in Irvine, CA

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