Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.
Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.
This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.
Extensive hands-on labs and an interview prep will help you land lucrative jobs.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
In this module you will learn how to install Python distribution - Anaconda, basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.
Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.
You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.
Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.
Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.
A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).
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 -
|4||Machine Learning and Artificial Intelligence|
|7||SQL database and coding|
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 -
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:
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:
Below are the right steps to become a successful data scientist:
Below are some effective ways to become a data scientist
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:
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:
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:
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.
|1.||The Business of Data Science - San Francisco||16 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 Conference||29 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
2. ODSC West 2019 - Open Data Science Conference, San Francisco
3. Data Science - 6/24 to 6/28, San Francisco
4. Women in Data Science (WiDS) Oakland, San Francisco
5. Data & Drinks, San Francisco
6. Health Data Sharing for Advanced Analytics, San Francisco
8. Data Science Fundamentals: Intro to Python, San Francisco
9. Data Analytics Talks (DAT), San Francisco
10. QB3 Seminar: Dennis Schwartz, Repositive, San Francisco
|1.||Deep Learning Summit, San Francisco||26 - 27 January, 2017||Park 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
2. Dataversity Smart Data Conference, San Francisco
3. AI By the Bay, San Francisco
4. Machine Intelligence Summit, San Francisco
Below are the steps to follow to get a data science job:
Follow the below steps to increase your chances of success for the job of Data Scientist-
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:
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:
A career path for a data scientist can be explained as follows:
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:
There are various job prospects for a data scientist in San Francisco–
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;
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;
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;
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;
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;
Follow these steps to successfully install Python 3 on your windows:
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:
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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Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.
Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.
By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.
Tools and Technologies used for this course are
There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are Python and data science experts who have years of industry experience.
Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor