Data Science with Python Training in San Francisco, CA, United States

Get the ability to analyze data with Python using basic to advanced concepts

  • 40 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

Description

Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.

Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.

This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.Extensive hands-on labs and an interview prep will help you land lucrative jobs.

What You Will Learn

Prerequisites

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.

Curriculum

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

Hands-on:

  • 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 

Hands-on:

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:

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

Hands-on: 

  • 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

Hands-on: 

  • 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
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price

Hands-on:  

  • 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

Hands-on:

 Project to be selected by candidates.

Meet your instructors

Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

View Profile

Projects

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

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 | odsc.com

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, H2O.ai
        • 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.

        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 https://raw.githubusercontent.com/Homebrew/install/master/install)" 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.

        reviews on our popular courses

        Review image

        I would like to extend my appreciation for the support given throughout the training. My special thanks to the trainer for his dedication, learned many things from him. KnowledgeHut is a great place to learn and earn new skills.

        Raina Moura

        Network Administrator.
        Attended Agile and Scrum workshop in May 2018
        Review image

        The customer support was very interactive. The trainer took a practical 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.

        Yancey Rosenkrantz

        Senior Network System Administrator
        Attended Agile and Scrum workshop in May 2018
        Review image

        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 supported throughout the process.

        Astrid Corduas

        Telecommunications Specialist
        Attended Agile and Scrum workshop in May 2018
        Review image

        I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and liked the way of teaching. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

        Ike Cabilio

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

        I really enjoyed the training session and extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.

        Tilly Grigoletto

        Solutions Architect.
        Attended Agile and Scrum workshop in May 2018
        Review image

        It is always great to talk about Knowledgehut. I liked the way they supported me until I get certified. I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and liked the way of teaching. My special thanks to the trainer for his dedication, learned many things from him.

        Ellsworth Bock

        Senior System Architect
        Attended Certified ScrumMaster®(CSM) workshop in May 2018
        Review image

        Trainer at KnowledgeHut made sure to address all my doubts clearly. I was really impressed with the training and I was able to learn a lot of new things. It was a great platform to learn.

        Meg Gomes casseres

        Database Administrator.
        Attended PMP® Certification workshop in May 2018
        Review image

        Trainer really was helpful and completed the syllabus covering each and every concept with examples on time. Knowledgehut also got good customer support to handle people like me.

        Sherm Rimbach

        Senior Network Architect
        Attended Certified ScrumMaster®(CSM) workshop in May 2018

        FAQs

        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. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

        • 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

        Have More Questions?

        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.