Data Science Course with Python in San Francisco, CA, 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
  • 220,000 + Professionals Trained
  • 250 + Workshops every month
  • 70 + Countries and counting

Grow your Data Science skills

This comprehensive hands-on course takes you from the fundamentals of Data Science to an advanced level in weeks. 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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  • 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 an Advanced Level

  • Code Reviews by Professionals

Data Scientists are in high demand across industries


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.

Continual Learning Support

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

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.


Prerequisites for the Data Science with Python training program

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

Who should attend this course?

Professionals in the field of data science

Professionals looking for a robust, structured Python learning program

Professionals working with large datasets

Software or data engineers interested in quantitative analysis

Data analysts, economists, researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course


Python Distribution

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


User-defined functions in Python

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


Datasets and manipulation

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


Probability and Statistics

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


Advanced Statistics

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


Predictive Modelling

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


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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Invest in forward-thinking data talent to leverage data’s predictive power, craft smart business strategies, and drive informed decision-making.

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Data Science with Python Course Curriculum

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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.


  • 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


  • 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


  • 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


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


  • 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.


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


  • 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 and classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study


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


  • 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.


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


  • 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.


  • Project to be selected by candidates.

FAQs on the Data Science with Python Course

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 and we will be happy to get back to you.

Data Science with Python

What is Data Science

It is a great time to be a data scientist in San Francisco. More and more companies are starting to see the potential of data science and incorporating it into their business. The companies that are looking for data scientists in San Francisco are Google, Oracle, LexisNexis, Twitter, Amazon, Diamond Foundry, PepsiCo, Paypal, Thunder, Genentech, etc.

San Francisco is home to several reputed institutions like Golden Gate University, University of San Francisco, University of the Pacific, etc. that offer a Master’s degree in Data Science. These courses will help you acquire the technical skills required to become a successful data scientist. A qualified data scientist is expected to be an expert in the following technical skills -

Sr. No.Skills
1Apache Spark
2Data Visualization
3Hadoop Platform
4Machine Learning and Artificial Intelligence
5Python Coding
6R Programming
7SQL database and coding
  1. Apache Spark: Apache Spark is a framework that helps in dispersing data. It is cluster computing platform designed to be fast and general-purpose.
  2. Data Visualization: Data visualization is used to understand the data using coherent representation. For this, tools like matplotlib, tableau, d3.js, and ggplot are used.
  3. Hadoop Platform: Knowledge of Hadoop platform is strongly recommended. It has several open-source software that helps in carrying out the development process smoothly.
  4. Machine Learning and Artificial Intelligence: Artificial intelligence and machine learning go hand in hand with data science. Here are some topics that you must have a thorough knowledge of:
    • Decision trees
    • Adversarial learning
    • Machine Learning algorithms
    • Logistic regression etc.
    • Reinforcement Learning
    • Neural Network
  5. Python Coding: Python is the most sought-after programming language used in the field of data science. It is a simple and versatile language that allows the data scientists to work with datasets easily.
  6. R Programming: R programming is extensively used by data scientists as well. It offers several libraries and packages that aid in analyzing data.
  7. SQL database and coding: SQL is used by data scientists to work with databases. It allows them to improve the structure of the database and get some information out of it.

As a Data Scientist, you need to have the clarity to make clear and informed decisions. Whether it is data analysis or writing codes, it is necessary for professionals to be clear about what to do and how to do it. Data Scientists must find innovative and creative ways to visualize data, develop new tools and methods etc. However, it is important to maintain a balance between creativity and rationality. Scepticism is a trait which helps keep Data Scientists on the right track without being distracted and carried away with creativity.

A data scientist is hailed as the  ‘Sexiest job of the 21st century’ as stated by Harvard Business Review. Companies in San Francisco have started to harness their data for insights for personalizing experience and acquiring and retaining customers. To convert companies’ data into action, data scientists are crucial. This is the reason why companies like Scaleapi, BICP, Bolt, Quantcast, Kinsa Inc., RiskIQ, Trainz, Eaze, Jyve, Brightidea, etc. are hiring data scientists.

Below are some of the top advantages of being a Data Scientist -

  1. Huge Pay: High pay is often seen as the first priority while searching for a job. Due to high demand and low supply, data scientists are rewarded handsomely.
  2. Large bonuses: Data Scientists get great bonuses and other perks may also include equity shares.
  3. Education: When you become a data scientist, you would usually be having either a Masters or a PhD. With such a high level of education, a Data Scientist will get good offers from corporate organization, colleges and universities as well as government institutions.
  4. Mobility: You will get an opportunity to work in other developed countries.

Data Scientist Skills and Qualifications

It is important for a Data Scientist to have good analytical problem-solving skills. Professionals must first understand and analyze the problem and then analytically find a solution to the problem. Communication skills are also essential as Data Scientists are required to communicate customer analytics and deep business strategies to companies. Also, to get a clear idea of what needs to be done, it is imperative to have updated industry knowledge. Without this, working in this field will be difficult and growth in the career will be stagnated.

These are the best ways to improve your data science skills for data scientist jobs:

  • Boot camps: Bootcamps are the perfect way to enhance your Python basics. There are several boot camps in San Francisco that you can look into.
  • MOOC courses: These are online courses where all types of courses related to Data Science are available on the internet.
  • Certifications: Certifications are short term courses which offer additional skills related to the field. Some famous and recognized Data Science Certificate courses include:
    • Cloudera Certified Professional: CCP Data Engineer
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
    • Cloudera Certified Associate - Data Analyst
  • Projects: Make sure that you are actively involved in projects. The more you manage projects, the more refined your thinking and capabilities will be.
  • Competitions: Lastly, contests like Kaggle, etc help in upgrading your knowledge. As these competitions offer a restrictive environment, it helps bring out innovative and creative ideas and solutions.

The dramatic increase in the demand for data scientists can be linked to the rise of Machine Learning and Artificial Intelligence. More and more students are opting for data science programs in universities as even with this growth in data scientists, there are not enough skilled applicants to fulfill the needs of the companies. Organizations like Google, Oracle, LexisNexis, Twitter, Amazon, Diamond Foundry, PepsiCo, Paypal, Thunder, Genentech, Scaleapi, BICP, Bolt, Quantcast, Kinsa Inc., RiskIQ, Trainz, Eaze, Jyve, Brightidea, etc. are willing to pay a handsome salary to a well-qualified data scientist.

A couple of approaches to practice your data science capacities are:

  • Beginner Level
    • Iris Data Set:  For pattern recognition, Iris Data Set is considered to be highly resourceful and versatile. It is easy to incorporate to learn the various classification techniques. For beginners in the field of data science, it is the best data set. It contains 50 rows along with 4 columns. Practice Problem: Predict the class of a blossom based on these parameters.
    • Loan Prediction Data Set: As compared to all other industries, the banking field uses data science and analytics most significantly. This data set can help a learner by providing an idea of the concepts in the field of insurance and banking. It contains 615 rows and 13 columns. 
      Practice Problem: Predict if a given advance will be endorsed by the bank or not.
  • Intermediate Level:
    • Black Friday Data Set: The Black Friday Data set refers to another set which caters to the retail sector. It captures the sales transactions from a retail store and analyzes the data to gain an understanding of the experiences of day to day shopping. The data set is set in order to explore and expand technical skills and capture the experiences of millions of customers. The set is considered a regression problem and has 550,069 rows and 12 columns.
      Practice Problem: Predict the amount of total purchase made.
    • Human Activity Recognition Data Set: The Human Activity Recognition Data Set has 561 columns and 10,299 rows and has a collection of 30 human subjects. Smartphone recordings were used to collect the subject data. The smartphones used to record the data had inertial sensors which helped in data collection.
      Practice Problem: Predict the human activity category.
  • Advanced Level:
    • Urban Sound Classification: A beginner in the field of Machine Learning can easily solve problems like Titanic survival prediction by using simple and very basic Machine Learning tools and methodologies. Unlike such problems, real problems are more complicated and complex which are harder to calculate, analyze and provide a solution for. The Urban Sound Classification data set helps in finding solutions to the real-world concept of Machine Learning. Along with this, it also helps in understanding, introducing and implementing the process of Machine Learning. The data set has 8,732 clippings which are categorized into 10 classes of urban sounds. The developer is introduced to real-world scenarios of classification and various concepts of audio processing.
      Practice Problem: Classify the type of sound that is obtained from a particular audio.

How to Become a Data Scientist in San Francisco, California

Below are the right steps to become a successful data scientist:

  1. Getting started: Select a programming language. We recommend R language or Python.
  2. Mathematics and statistics: The science in data science is connected to dealing with the data (maybe numerical, printed or an image), making models and associations between them. 
  3. Data visualization: Without a creative approach, data visualization will not be possible. Understanding, Analyzing and Simplifying the data for non-technical team members requires extensive data visualization.
  4. ML and Deep learning: In-depth knowledge of Machine Learning and Artificial Intelligence will help you be more efficient and productive.

Below are some effective ways to become a data scientist

  1. Degree/certificate: Get a degree or certification. It is needed for you to get documented proof of your knowledge.
  2. Unstructured data: The job of a data scientist boils down to discovering patterns in data. Usually, the data is unstructured and doesn’t fit into a database. This step has the highest complexity due to the sheer amount of work involved to structure the data and make it useful. Your job is to understand and manipulate this unstructured data.
  3. Software and Frameworks: As you will be involved with incredible amounts of unstructured data, you must familiarize yourself with software and frameworks of the field.R is still the most used language for statistical problems. Hadoop framework is used by scientists when the data exceeds the memory at hand. It quickly conveys the data into the machine. Spark is fast becoming popular due to its advantage of speed. Apart from computing faster, it also prevents the loss of data. You must be proficient in SQL queries as the knowledge of database is just as important as the framework and language.
  4. Machine learning and Deep Learning:  Machine learning is all about the implementation of the textbook concepts to the real-world for better analysis and growth.
  5. Data visualization: Representing data in a coherent fashion is important to make informed business decisions. You will have to make sense out of a huge pile of unstructured data to make the right decisions for the betterment of your company.

Institutions like Golden Gate University, University of San Francisco, University of the Pacific, etc. are offering Master’s degree in Data Science. As mentioned before, approximately 46% of all data scientists are PhD degree holders and 88% of data scientists hold a Master’s degree. While looking for the degree, you will find the opportunity to network, which will indubitably increase your chances in landing a relevant job. You will also get an internship opportunity with various leading companies.

If your total is more than 6 points, we advise you to pursue a Masters degree:

  • You have a strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • You have a weak STEM background ( biochemistry/biology/ economics or another similar degree/diploma): 2 points
  • You belong to a non-STEM background: 5 points
  • You have less than 1 year of experience in working with Python programming language: 3 points
  • You have never been part of a job that requires you to code on a regular basis: 3 points
  • You think you are not good at independent learning: 4 points
  • You do not understand what it means when we tell you that this scorecard is a regression algorithm: 1 point

Knowledge of programming is perhaps the most key factor while exploring the career option of data science. Below are some reasons why it is important to have programming knowledge:

  • Data sets: Data science incorporates overseeing a lot of data sets. Knowledge of programming helps a data scientist in the evaluation of huge data sets.
  • Statistics: An understanding of multivariable calculus and linear algebra is essential for a data scientist. Examples of Statistical Learning problems include:
    • Identify the risk factors for prostate cancer.
    • Predict whether someone will have a heart attack on the basis of demographic, diet and clinical measurements.
    • Establish the relationship between salary and demographic variables in population survey data.
    • Customize an email spam detection system.
  • Framework:  If, as a data scientist, you want to perform data analysis properly and efficiently, your programming ability will help you a lot. You will be able to build a system according to the needs of the organization. You will be able to create a framework that could not only automatically analyze experiments, but also manage the data visualization process and the data pipeline. This is done to make sure that the data can be accessed by the right person at the right time.

Data Scientist Salary in San Francisco, California

The average annual salary of a Data Scientist in  San Francisco is $119,953.

The average yearly income of data scientist in San Francisco is $24,692 than Austin. 

As compared to Los Angeles, Data Scientist in New York earns $119,953 per year, which is significantly higher than a data scientist working in Los Angeles at an income of $98,294 per year. 

The average annual salary of a data scientist in Seattle is $92,966, which is $26,987 less than that of San Francisco. 

The annual salary of a Data Scientist in  Los Angeles is $98,294. 

The city of San Diego offers a data scientist an average pay of $118,007 which is almost equal to the salary earned by data scientists in San Francisco. 

Apart from San Francisco, the city of Sacramento in California has an average pay of $121,590 per year for data scientists. 

The demand for Data Scientists in California is high. This is because of major and minor organizations working to build a team that can convert raw data into useful business insights.

Being a Data Scientist in San Francisco offers the following benefits:

  1. Job growth
  2. Several job opportunities
  3. Ability to work in the field of interest

Data Scientist is the hottest job right now. Needless to say, it comes with its own perks and advantages. Apart from salary, the advantages of being a data scientist include access to top-level management. This is because data scientists play a key role in providing useful business insights from raw data. Also, data scientists can work for any field they are interested in because every company in every field produces data that needs to be deciphered.

Companies hiring Data Scientists in San Francisco include Airbnb, The Climate Corporation and Qordoba. 

Data Science Conference in San Francisco, California

S.NoConference nameDateVenue
1.The Business of Data Science - San Francisco16 July, 2019 to 17 July, 2019

Hyatt Centric Fisherman's Wharf San Francisco 555 North Point St San Francisco, CA 94133 United States

2.ODSC West 2019 - Open Data Science Conference29 Oct, 2019 to 1 Nov, 2019

Hyatt Regency San Francisco Airport 1333 Old Bayshore Highway Burlingame, CA 94010 United States

3.Data Science - 6/24 to 6/28
24 June, 2019 to 28 June, 2019

Code for fun learning center 6600 Dumbarton Circle Fremont, CA 94555 United States

4.Women in Data Science (WiDS) Oakland
May 8, 2019

The California Endowment's Center for Healthy Communities 2000 Franklin Street Elmhurst Room, 2nd Floor Oakland, CA 94612 United States

5.Data & Drinks
May 7, 2019

Snowflake Computing 450 Concar Drive San Mateo, CA 94402 United States

6.Health Data Sharing for Advanced Analytics
June 12, 2019

WeWork 2 Embarcadero Center San Francisco, CA 94111 United States

7.Big Data in Precision Health
22 May, 2019 to 23 May, 2019
Li Ka Shing Learning and Knowledge Center 291 Campus Drive Stanford, CA 94305
8.Data Science Fundamentals: Intro to Python
3 June, 2019 to 8 July, 2019

Galvanize- San Francisco 44 Tehama St San Francisco, CA 94105 United States

9.Data Analytics Talks (DAT)
May 3, 2019

San Francisco State University Downtown Campus 835 Market Street, Room 597, 5th floor San Francisco, CA 94103 United States

10.QB3 Seminar: Dennis Schwartz, Repositive
June 13, 2019

Room N-114, Genentech Hall 600 16th St. UCSF Mission Bay San Francisco, CA 94158 United States

1. The Business of Data Science - San Francisco

  • About the conference: The conference will help you learn how to use Data Science and AI for your organization.
  • Event Date: 16 July, 2019 to 17 July, 2019
  • Venue: Hyatt Centric Fisherman's Wharf, San Francisco 555 North Point St San Francisco, CA 94133 United States
  • Days of Program: 2
  • Timings: Tue, Jul 16, 2019, 9:00 AM – Wed, Jul 17, 2019, 4:30 PM PDT
  • Purpose: The purpose of the conference is to help the business leaders understand the fundamentals of Data Science.
  • Registration cost: $1,725 – $2,190
  • Who are the major sponsors: Pragmatic Institute

2. ODSC West 2019 - Open Data Science Conference, San Francisco

  • About the conference: The conference is all about learning new skills required to accelerate your career and networking with the data science community.
  • Event Date: 29 Oct, 2019 to 1 Nov, 2019
  • Venue: Hyatt Regency San Francisco Airport, 1333 Old Bayshore Highway, Burlingame, CA 94010, United States
  • Days of Program: 4
  • Timings: Tue, Oct 29, 2019, 9:00 AM – Fri, Nov 1, 2019, 6:00 PM PDT
  • Purpose: The purpose of the conference is to offer talks, workshops and hands-on training in Artificial Intelligence and Data Science.
  • Registration cost: $1,196 – $5,196
  • Who are the major sponsors: ODSC Team |

3. Data Science - 6/24 to 6/28, San Francisco

  1. About the conference: This seminar is for students who want to start a career in Data Science. They will learn how to analyze data and answer the question. 
  2. Event Date: 24 June, 2019 to 28 June, 2019
  3. Venue: Code for fun learning center 6600 Dumbarton Circle Fremont, CA 94555 United States
  4. Days of Program: 1
  5. Timings: Mon, Jun 24, 2019, 9:00 AM – Fri, Jun 28, 2019, 3:00 PM PDT
  6. Purpose: The purpose of the seminar is to build a solid foundation of math and statistics in students.
  7. Registration cost: $150 – $420
  8. Who are the major sponsors: Code for fun

4. Women in Data Science (WiDS) Oakland, San Francisco

  • About the conference: The conference will feature female speakers working in the field of Data Science.
  • Event Date: May 8, 2019
  • Venue: The California Endowment's Center for Healthy Communities, 2000 Franklin Street, Elmhurst Room, 2nd Floor, Oakland, CA 94612, United States
  • Days of Program: 1
  • Timings: 10:30 AM – 3:00 PM PDT
  • Purpose: The purpose of the conference is to use the data to measure social impact and analyse this data for policy and legislative change.  
  • How many speakers: 3
  • Speakers & Profile: 
    • Maria Kei Oldiges, Social Impact Research and Evaluation Director - Beneficial State Foundation
    • Kristina Williams, Tech Founder & CEO - CULTURxEAT and Zim Art
    • Olivia Cueva, Creative Director - David E. Glover Education and Technology Center
  • Registration cost: Free

5. Data & Drinks, San Francisco

  • About the conference: The conference focuses on Data Economy. The panel will discuss how Data is powering every digital experience that we have.
  • Event Date: May 7, 2019
  • Venue: Snowflake Computing 450 Concar Drive San Mateo, CA 94402 United States
  • Days of Program: 1
  • Timings: 6:00 PM – 8:00 PM PDT
  • Purpose: The purpose of the conference is to know what data economy means to the society and how this data has changed the industry.
  • How many speakers: 3
  • Speakers & Profile:
    • Emil Eifrem - CEO & Co-founder, Neo4j
    • Eva Nahari - Director of Product, Cloudera
    • Christian Finstad - VP Sales & Customer Success, Meltwater
  • Registration cost: $15 – $30
  • Who are the major sponsors: The Swedish-American Chamber of Commerce in San Francisco & Silicon Valley

6. Health Data Sharing for Advanced Analytics, San Francisco

  • About the conference: The conference’s primary focus is on the importance of health data exchange. The panels will discuss how important is the incorporation of real-world data for patient recruitment in trials and feasibility assessment of a protocol.
  • Event Date: June 12, 2019
  • Venue: WeWork, 2 Embarcadero Center, San Francisco, CA 94111, United States
  • Days of Program: 1
  • Timings: 6:00 PM – 7:30 PM PDT
  • Purpose: The purpose of the conference is to understand how can we better understand the social determinants of health using demographic and consumer data.
  • How many speakers: 2
  • Speakers & Profile:
    • Bob Borek - Head of Marketing, Datavant
    • Aneesh Kulkarni - Head of Engineering, Datavant
  • Registration cost: Free
  • Who are the major sponsors: SF Health Tech and Health Data
  1. Big Data in Precision Health, San Francisco

8. Data Science Fundamentals: Intro to Python, San Francisco

  • About the conference: This is a Data Science course that will help you learn the basics of python.
  • Event Date: 3 June, 2019 to 8 July, 2019
  • Venue: Galvanize- San Francisco 44 Tehama St San Francisco, CA 94105 United States
  • Days of Program: 36
  • Timings: Mon, Jun 3, 2019, 6:30 PM – Mon, Jul 8, 2019, 7:30 PM PDT
  • Purpose: The purpose of the course is to understand the nuances of Python and how to use them in Data Science projects.
  • Registration cost: $1,890
  • Who are the major sponsors: Galvanize San Francisco SoMa

9. Data Analytics Talks (DAT), San Francisco

10. QB3 Seminar: Dennis Schwartz, Repositive, San Francisco

  • About the conference: The conference is going to deal with the new challenges faced by scientists in identifying cancer drug tests.
  • Event Date: June 13, 2019
  • Venue: Room N-114, Genentech Hall, 600 16th St. UCSF Mission Bay San Francisco, CA 94158 United States
  • Days of Program: 1
  • Timings: 12:00 PM – 1:00 PM PDT
  • Purpose: The purpose of the conference is to overcome the challenges in identifying the cancer drug targets and find a way for the validation of potential targets.
  • How many speakers: 1
  • Speakers & Profile: Dennis Schwartz - software developer and bioinformatician
  • Registration cost: $0 – $10
  • Who are the major sponsors: QB3

S.NoConference nameDateVenue
1.Deep Learning Summit, San Francisco 26 - 27 January, 2017Park Central Hotel, 50 3rd St, San Francisco, CA 94103, United States
2.Dataversity Smart Data Conference 
30 Jan - 1 Feb, 2017
Pullman San Francisco Bay, 223 Twin Dolphin Drive, Redwood City, California
3.AI By the Bay
 6-8 March, 2017
PEARL, 601 19th St. San Francisco, CA 94107
4.Machine Intelligence Summit
23-24 March, 2017
South San Francisco Conference Center, 255 S Airport Blvd, South San Francisco, CA 94080

1. Deep Learning Summit, San Francisco

  • About the X conference: The conference invited around 40 speakers to discuss the challenges in the research and application of deep learning.
  • Event Date: 26 - 27 January, 2017
  • Venue: Park Central Hotel, 50 3rd St, San Francisco, CA 94103, United States
  • Days of Program: 2
  • Timings: 8 A.M. to 5 P.M.
  • Purpose: The conference brought together leading innovators from different fields to explore the advances in deep learning algorithms and technologies.
  • How many speakers: 15
  • Speakers & Profile:
    • Ian Goodfellow - Staff Research Scientist, Google Brain
    • Brendan Frey - Co-Founder & CEO, & Professor, University of Toronto
    • Shivon Zilis - Partner, Bloomberg
    • Andrej Karpathy - Director of Artificial Intelligence, Tesla
    • Andrew Tulloch - Research Engineer, Facebook
    • Ofir Nachum - Research Engineer, Google Brain
    • Stefano Ermon - Assistant Professor, Stanford University
    • Toru Nishikawa - CEO, Preferred Networks
    • Avidan Akerib - VP of the Associative Computing, GSI Technology
    • Durk Kingma - Research Scientist, OpenAI
    • Eli David - CTO, Deep Instinct
    • Roland Memisevic - Chief Scientist, Twenty Billion Neurons
    • Sergey Levine - Assistant Professor, UC Berkeley
    • Chris Moody - Data Scientist, StitchFix
    • Rumman Chowdhary - Senior Principal, Accenture
    • Who were the major sponsors:
      • Preferred Networks
      • GSI Technologies
      • Intel Nervana
      • Qualcomm

      2. Dataversity Smart Data Conference, San Francisco

      • About the X conference: The conference brought together all levels of technical understanding in the emerging field of data science. It focused on intelligent information gathering and analysis. 
      • Event Date: 30 Jan - 1 Feb, 2017
      • Venue: Pullman San Francisco Bay, 223 Twin Dolphin Drive, Redwood City, California
      • Days of Program: 3
      • Timings: 8:30 A.M. to 5:30 P.M.
      • Purpose: The conference focused on all aspects of emerging technologies in Data Science and related fields like Big Data, IoT, NLP, Machine Intelligence, Machine Learning, Deep Learning, Cognitive Computing, etc.
      • How many speakers: 12
      • Speakers & Profile:
        • Kirk Borne - Booz Allen Hamilton
        • Douglas Lynat - Cycorp, Inc. 
        • Bob Touchton - Leidos
        • Erik T. Mueller - Capital One
        • Ben Goertzel - Novamente LLC.
        • TomJacobs - Adobe
        • Dean Allemang - Working Ontologist, LLC
        • Emil Eifrem - Neo Technology
        • Oliver Hesse - Bayer Pharmaceuticals
        • Jans Aasman - Franz Inc.
        • Scott Purdy - Numenta
        • Barry Zane - Cambridge Semantics

      • Who were the major sponsors:
        • Oracle
        • Data Ninja
        • Neo4j
        • Numenta
        • Cambridge Intelligence
        • Cambridge Semantics
        • Expert System
        • Linkurious

      3. AI By the Bay, San Francisco

      • About the X conference: The conference defined Artificial Intelligence in the context of enterprises and startups.
      • Event Date: 6-8 March, 2017
      • Venue: PEARL, 601 19th St. San Francisco, CA 94107
      • Days of Program: 3
      • Purpose: The purpose of the conference was to bring together innovators from different areas who have built companies from scratch and are at a point where they can see the future and the upcoming technologies.
      • How many speakers: 13
      • Speakers & Profile:
        • Alexy Khrabrov - Chief Scientist and Founder, By the Bay
        • Joel Horwitz - Vice President, Ecosystem & Partnership Development, IBM
        • Vitaly Gordon -  VP of Engineering and Data Science, Salesforce Einstein
        • Adam Gibson - CTO, Skymind
        • Stephen Merity - Senior Research Scientist, Salesforce Research
        • Arno Candel - Chief Architect,
        • Chris Fregly - Founder, Research Engineer, PipelineIO
        • Mike Tamir - Chief Data Science Officer, Uber ATG
        • Chris Moody - Scientist, Stitch Fix
        • Feynman Liang - Director of Engineering, Gigster
        • Eduardo Ariño de la Rubia - Chief Data Scientist in Residence, Domino Data Lab
        • Michael Ludden - IBM Watson Developer Labs Program Director, IBM
        • Sara Asher - Director of Product at Salesforce Einstein, Salesforce
      •   Who were the major sponsors:
        • IBM
        • Salesforce
        • Crowd Flower
        • Data Collective
        • Comet Labs
        • Domino
        • Uber
        • Bosch
        • Data Monster

        4. Machine Intelligence Summit, San Francisco

        • About the X conference: The conference focused on managing and deploying models of Machine Learning.
        • Event Date: 23-24 March, 2017
        • Venue: South San Francisco Conference Center, 255 S Airport Blvd, South San Francisco, CA 94080
        • Days of Program: 2
        • Purpose: The purpose of the conference was to explore Deep Learning and AI technologies.
        • How many speakers: 11
        • Speakers & Profile:
          • Melody Guan - Deep Learning Resident, Google Brain
          • Nick Pentreath - Principal Engineer, IBM
          • Robinson Piramuthu - Chief Scientist of Computer Vision
          • Amy Gershkoff - Chief Data Officer, Ancestry
          • Abi Komma - Senior Data Scientist, Uber
          • Minjoo Seo - Ph.D. Student, University of Washington
          • Alex Brokaw - Writer, Freelance
          • Cory Kidd - CEO, Catalia Health
          • Chris Slowe - Founding Engineer, Reddit
          • Erik Schmidt - Senior Scientist, Pandora
          • Kevin Hightower - Director of Product, Airmap

        Data Scientist Jobs in San Francisco, California

        Below are the steps to follow to get a data science job:

        1. Getting started - First of all, choose a programming language you are comfortable with, like Python or R language. Then, get familiar with the job, roles, and responsibilities of a data scientist to understand your role better.
        2. Mathematics - Data science deals with making coherent analysis out of raw data which might not make a lot of sense on its own. You need to have good command over mathematics to be comfortable in data science.
        3. Libraries - Processing raw data into a structured data set includes real-life application of machine learning techniques. Some famous libraries are Scikit-learn, SciPy, NumPy, Pandas, etc.
        4. Data Visualization - A data scientist is expected to make coherent and presentable content out of raw, unstructured data. One of the most popular ways to present data has been the graph. Some commonly used ones are Matplotlib - Python and Ggplot2 - R.
        5. Data Processing - To make it presentable and usable, it becomes important to process the data right. Data scientists need to know how to apply machine learning concepts to real-world practical problems smartly and make them analysis-ready.
        6. Machine learning and deep learning - Invest some time in deep learning to go with your basic machine learning skills to make for an impressive resume. You can get familiar with neural networks, CNN and RNN.
        7. Natural language processing - You must be good with NLP, which involves processing and classification of the text form of the data.
        8. Polishing skills - Keep brushing your skills from time to time to keep yourself in touch with your knowledge of the field. Competitions like Kaggle are a great way to do that. You can also experiment on your own with personal projects.

        Follow the below steps to increase your chances of success for the job of Data Scientist-

        • Study: To clear an interview, incorporate all essential topics, including-
          • Probability
          • Statistics
          • Statistical models
          • Machine Learning
          • Understanding neural networks
        • Meetups and conferences: Start growing your network or increasing your professional relationships by visiting Tech meetups and data science conferences.
        • Competitions: Implement, test and continue improving your aptitude by taking an interest in online competitions like Kaggle.
        • Referral: It was reported in a survey that in data science companies, referrals are the main source of interviews. So, be assured to keep your LinkedIn profile updated.
        • Interview: Once you are sure that everything written above is done, be confident and go for the interview.

        Data has become an integral part of our lives. Tons of data is generated every day which is a goldmine of ideas and insights. It is the responsibility of a data scientist to process this data and use it to improve the business. Here are some other roles and responsibilities of a data scientist:

        Data Scientist Roles and Responsibilities:

        • Determining the correct data sets and variables
        • Cleaning and organizing the data
        • Applying models and algorithms to mine big data
        • Analyzing the data to identify patterns and trends
        • Interpreting the data to get results

        The role of a data scientist is touted to be the 21st century's hottest job. The salary of a data scientist varies based on two factors:

        • Type of company
          • Startups: Highest pay
          • Public: Medium pay
          • Governmental & Education sector: Lowest pay
        • Roles and responsibilities
          • Data scientist: $130,000/yr
          • Data analyst: $99,606/yr

        A career path for a data scientist can be explained as follows:

        • Business Intelligence Analyst: A Business Intelligence Analyst works closely with IT teams to turn data into critical information and knowledge that can be used to make critical business decisions.
        • Data Mining Engineer: The role of a Data Mining Engineer involves determining processes for centralizing collected data from numerous databases while ensuring these databases are linked
        • Data Architect: The role of the data architect is to manage data. Data architects define how the data will be stored, consumed, and managed by different data entities and IT systems.
        • Data Scientist: The role of a data scientist is to extract meaning from and interpret data. It is a vital combination of Cleaning, Interpreting and Transforming the data.

        Below are the best-acknowledged organisations for data scientists in San Francisco –

        • Salesforce Data Analytics
        • NextAI
        • SF Big Analytics
        • Metis: San Francisco Data Science
        • BlobCity Meet | SF Bay Area

        The most practical way to ensure a job is through Referrals. Some of the different ways to network with data scientists in San Francisco are:

        • Data science conference
        • An online platform like LinkedIn
        • Social gatherings like Meetup

        There are various job prospects for a data scientist in San Francisco–

        • Data Scientist
        • Data Architect
        • Data Administrator
        • Data Analyst
        • Business Analyst
        • Marketing Analyst
        • Data/Analytics Manager
        • Business Intelligence Manager

        Data Science with Python San Francisco, California

        Python is a Multi-paradigm programming language. Python is one of the most commonly preferred languages preferred by Data Scientists because of its simplicity and readability. It is a structured programming language that comes with several packages and libraries that can be beneficial in the field of Data Science. It also comes with a diverse range of resources. So, anytime you are stuck, you have these resources at your disposal.

        R Programming: R is one of the most frequently used programming tools for data science. It is an open source software that allows users to compute huge data sets, get statistical insights, create custom graphics and more. The platform is a bit advanced for first-time users but extremely effective and accurate once you get the hang of it. It includes; 

        • Top-notch data packages, statistical analysis models, optimized templates,
        • Functionalities such as public R package which is connected to over 8000 networks, Microsoft RStudio and more
        • Viva GGPLOT, Visual tools and a great interface for smooth matrix handling  

        Python: Python is a very popular, dynamic and versatile language 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. Some perks of using Python are; 

        • Open source platform is easy to customize 
        • Optimized for most devices, and compatible with almost every operating system hence easy to access 
        • Comes with special features like Scikit learn, sensor flow and Pandas for quick and effective data analysis 

        SQL: SQL or structured query language is a mandatory tool that every data scientist must master. It is used for editing, customizing and arranging information in relational databases. SQL is used for storing databases, retrieving old data sets, and for gaining quick and immediate insights. Other perks include; 

        • A user-friendly interface that comes with a comprehensive syntax 
        • Quick and time-saving, it's very easy to sort, create tables, curate data, manipulate queries and more

        Java: JAVA is a well-known programming language that runs on the JVM or Java Virtual Machine Platform. Most MNCs and Corporations use Java to create backend systems and applications. Some advantages of using Java are; 

        • Java is an extremely compatible and comprehensive platform which runs on OOPS framework and hence is easy to customize. 
        • Users can edit and design codes for both frontend and backend applications 
        • Plus, it is easy to compile data using Java 

        Scala: Scala also runs on JVM and is an ideal choice for data scientists to run massive data sets. It also comes with a fully functional coding interface and a powerful static tape framework; 

        • Scala supports Java and other OOPS platforms 
        • It is also used along Apache Spark and other high-performance programming languages.

        Follow these steps to successfully install Python 3 on your windows:

        • Go to the download page and set up the python program on your windows via GUI installer.
        • Open it and select the checkbox at the bottom asking you to add Python 3.x to PATH. It will allow you to use python’s functionalities from the terminal.

        Or you can also install python via Anaconda if you wish to.

        Note: You can also install virtualenv to your computer to create isolated python environments and pipenv - a python dependency manager.

        You can download and install Python 3 from the official website by using a .dmg package. However, we recommend using Homebrew to install python along with its dependencies. To install python 3 on Mac OS X, follow these 3 steps:

        1. Install xcode: To install brew, type in the following command: $ xcode-select --install
        2. Install brew: Install the package manager for Apple, Homebrew, using the following command: 
          /usr/bin/ruby -e "$(curl -fsSL" To confirm that it is installed, type: brew doctor
        3. Install python 3: Install the latest version of python and type: 
          brew install python
        4. To confirm its version, use: python --version

        We recommend that you also install virtualenv, which will help you in creating isolated places to help run different projects. It will also be helpful when using different Python versions.

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        What Learners Are Saying

        Ong Chu Feng Data Analyst
        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!

        Attended Data Science with Python Certification workshop in January 2020

        Matt Davis Senior Developer

        The learning methodology put it all together for me. I ended up attempting projects I’ve never done before and never thought I could.

        Attended Full-Stack Development Bootcamp workshop in May 2021

        Nathaniel Sherman Hardware Engineer.

        The KnowledgeHut course covered all concepts from basic to advanced. My trainer was very knowledgeable and I really liked the way he mapped all concepts to real world situations. The tasks done during the workshops helped me a great deal to add value to my career. I also liked the way the customer support was handled, they helped me throughout the process.

        Attended PMP® Certification workshop in April 2020

        Vito Dapice Data Quality Manager

        The trainer was really helpful and completed the syllabus on time and also provided live examples which helped me to remember the concepts. Now, I am in the process of completing the certification. Overall good experience.

        Attended PMP® Certification workshop in April 2020

        Issy Basseri Database Administrator

        Knowledgehut is the best training institution. The advanced concepts and tasks during the course given by the trainer helped me to step up in my career. He used to ask for feedback every time and clear all the doubts.

        Attended PMP® Certification workshop in January 2020

        Astrid Corduas Telecommunications Specialist

        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 and smoothed out the whole process.

        Attended Agile and Scrum workshop in June 2020

        Archibold Corduas Senior Web Administrator

        I feel Knowledgehut is one of the best training providers. Our trainer was a very knowledgeable person who cleared all our doubts with the best examples. He was kind and cooperative. The courseware was excellent and covered all concepts. Initially, I just had a basic knowledge of the subject but now I know each and every aspect clearly and got a good job offer as well. Thanks to Knowledgehut.

        Attended Agile and Scrum workshop in February 2020

        Rafaello Heiland Prinicipal Consultant

        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 future projects.

        Attended Agile and Scrum workshop in April 2020

        Data Science with Python Certification Course in San Francisco, CA

        The Golden Gate Bridge, the boutiques along Fillmore Street, the cool summers, the fog and the fabulousness, all describe this city in California. Completely destroyed by a massive earthquake in the early 20th century, the city was rebuilt and its renovation in terms of architecture and a financial centre continued up until the 1980?s, as a result of which today it is the undisputed leader in economics and modern high rises. It is home to several leading national and international banks including Wells Fargo, Federal Reserve Bank, Bank of America and several others. Biotechnology, research, and technology companies have also seen a huge rise due to its payroll tax exemption policies. KnowledgeHut offers several courses that help you kick start your career in San Diego 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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