Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.
Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.
This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.
Extensive hands-on labs and an interview prep will help you land lucrative jobs.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
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Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
In this module you will learn how to install Python distribution - Anaconda, basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.
Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.
You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.
Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.
Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.
A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).
Data Science includes exploring data at the microscopic level to understand the complex trends, behavior, and inferences which help companies to make better and smarter decisions based on the results obtained. Data Scientists analyze data to understand the pattern and characteristics of data by applying techniques like synthetic control experiments, inferential models, time series forecasting, segmentation analysis, etc.
According to reports from Linkedin, Data scientists is the no. 1 most promising job in America for 2019. Some of the common data scientist job titles are as follows:
Seattle, WA is considered to be the fastest growing city in the US. It has a strong job market and a tech space that is in dire need of data scientists. Companies like Google, Amazon.com, ExtraHop Networks, Zillow Group, T-Mobile, Weyerhaeuser, NordStrom, TenPoint7, etc. are looking for data scientists to analyze their data and find useful insights that can optimize their business processes. The reasons for the popularity of Data Science as a career choice are as follows:
There are several colleges in Seattle, WA where you could earn a degree in Data Science and get all the technical skills required to be a Data Scientist. Colleges like City University of Seattle and Seattle University are known for their Master’s degree program in Data Science.
The top skills that are needed to become a data scientist include the following:
1. Programming/Software: Programming languages and software packages are top skills necessary to be possessed by the data scientists to extract, clean, analyze, and visualize data efficiently. The main programming languages that an aspiring data scientist should be familiar with are as follows:
2. Hadoop Platform: Hadoop is an open-source software framework and is heavily preferred in several data science projects for processing of large data sets. It can store unstructured data such as text, images, and video. Hadoop is equipped with features like flexibility, scalability, fault tolerance, and low cost which makes it a preferable choice for data scientists.
3. Statistics/ Mathematics: A concrete understanding of multivariable calculus and linear algebra is essential for a data scientist since it forms the basis of many data analysis techniques. Math is considered to be the second language for data scientists since it simplifies writing algorithms. Data interpretation requires a deep understanding of correlations, distribution, maximum likelihood estimators and so much more.
4. Machine Learning and Artificial Intelligence: Machine Learning requires a better understanding of neural networks, reinforcement learning, adversarial learning, etc. It can be considered as a subset of Artificial Intelligence but focuses on making predictions from data available from past experiences. Machine Learning connects Artificial Intelligence with Data Science. Artificial Intelligence focuses on understanding core human abilities such as speech, vision, decision making, language, and other complex tasks, and designing machines and software to emulate these processes through techniques like Computer vision, language processing, and machine learning.
5. Data Cleaning: It is important that the data is correct and accurate before data scientists analyze it. Therefore, a considerable amount of time and effort is spent to ensure this. Data cleaning also termed as data cleansing is identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing it with the correct data. Tools like Trifacta, OpenRefine, Paxata, Alteryx, Data Ladder, WinPure are used for data cleaning. Therefore, data quality should possess the features of accuracy, validity, completeness, uniformity, and consistency.
6. Apache Spark: Apache Spark is a fast and general-purpose cluster computing system designed to cover a wide range of workloads such as interactive queries, batch applications, streaming and, iterative algorithms. The top highlighted features of Apache Spark are as follows:
The important feature of Spark is its in-memory cluster computing that increases the processing speed hence provides fast computation.
7. Data visualization: Data visualization tools provide a better and accessible way to see and understand trends, outliers, and patterns in data by using visual elements like maps, graphs, and charts. A good and effective data visualization tool make large data sets coherent. The main focus of data visualization is on information presentation which is achieved through the following:
8. Unstructured Data: Unstructured data can be defined as data that cannot fit neatly into a database and does not follow the conventional data model like Word documents, email messages, PowerPoint presentations, survey responses, transcripts of call center interactions, and posts from blogs and social media sites. Working with unstructured data provides a better insight into analyzing data.
Below are the top 5 behavioral traits of a successful Data Scientist -
Data Scientists are in high demand in Seattle, WA right now. The reason behind this is that the city is home to several big tech corporations and there are not enough data scientists to help harness the data these companies process. These companies include Tableau, Thunder, QVC, Logic 20/20, Facebook, Brillio, Convoy, Microsoft, etc.
A Harvard Business Review article labeled “data scientist” as the sexiest job of the 21st century. Some of its benefits can be summarised as follows:
Below is the list of top business skills needed to become a data scientist:
1. Analytic Problem-Solving – You must possess a data-driven mindset to understand the problem. You must be able to select relevant information and ask the right questions in order to match the pace of your organization. Sometimes, the available information is sufficient enough to guide you to the solution. But at times, you need to use your own problem-solving skills by using all the knowledge you acquire, so you should be able to handle the issues on your own.
2. Communication Skills – Communication skill is very important in data science. In other terms, the way you communicate your findings to an audience of non-data scientists is as important as the findings themselves. As a data scientist, you may have the best idea but it is of no value if you are unable to communicate those ideas to executives, managers, or your colleagues.
3. Business acumen – It is important to understand the business requirements of the organization you are working with, on a broader scale. You must understand how your business operates and how these techniques will be applied in real time so that your solutions fit accordingly. It also helps to categorize the problems on the grounds of priority.
4. Teamwork – A data scientist works in a team of people with different roles from different sectors including business, finance, marketing, technology, etc. and each of the members of the team contributes to the development of the work going on. It is important to understand the team spirit and maintain a jovial and professional bond with the people to promote overall growth and success.
Below are the best ways to brush up your data science skills for data scientist jobs:
Seattle, WA is considered to be the fastest growing city in the US where Data Scientists are in high demand in Seattle right now. It has a strong job market and a tech space in dire need of data scientists. The reason behind this is that the city is home to several big tech corporations and there are not enough data scientists to help harness the data these companies process. The companies that are hiring Data Scientists in Seattle, WA include Google, Amazon.com, ExtraHop Networks, Zillow Group, T-Mobile, Weyerhaeuser, NordStrom, TenPoint7, Tableau, Thunder, QVC, Logic 20/20, Facebook, Brillio, Convoy, Microsoft, etc.
The best ways to practice your Data Science skills are as follows:
Below are the right steps to becoming a successful data scientist:
1. Choose an academic path: It is important to have a Bachelor’s degree in Computer Science or a related field. More and more data scientists are opting for master's degrees and Ph.D. This depends on the role offered by the company. So you must choose your academic path accordingly and choose your specialization according to your interest.
2. Mathematics and statistics: Key concepts in statistics include:
You should have your basics cleared in statistics. A good understanding of maths helps you to write a better algorithm.
3. Fundamentals: You must have your basics cleared in Data Science which will help you build the foundation for future learning.
4. Programming: It is very essential to have the following programming knowledge:
5. Specializations: Choose your field of interest and the technology that you want to build your career in. Gain proficient knowledge in one or more of the following technologies:
6. Apply for jobs: Follow up with all the notifications posted by your desired company and keep a check on the posts available. Apply for the jobs you find yourself suitable. You can contact experienced professionals and look up the various interview questions and tips to apply for these jobs.
The essential steps that you should follow to become a data scientist are as follows:
Institutions like City University of Seattle and Seattle University have Data Science programs that will help you kick start your career in the Data Science field. These courses will introduce you to the Data Science and how you can apply its concepts in the real world.A degree in data science important because of the following –
A few institutes have built undergraduate programs that will be akin to a computer science degree. Colleges like City University of Seattle and Seattle University are known for their Master’s degree program in Data Science. Based on the trends in job requirements, the skills in most demand are Hadoop/Big Data, tools including R and SAS, and some domain knowledge. Theoretical knowledge is a prerequisite, but usually good data selection and engineering are more important than advanced algorithms.
However, there is a strong demand for analytic talent and a shortfall in the supply of skilled employees. If you have a master's degree, it will be add-on for you but if you don't have, many companies will overlook this as long as you have the right skills.
Programming in Data Science is the key skill to have in order to become a Data Scientist. Coding is involved in so many procedures in Data Science. Some of these are as follows:
The average annual salary for a Seattle based Data Scientist is $106,466.
The average annual salary of a data scientist in New York is $99,716, which is $6,750 less than that of Seattle.
The earning of a Data Scientist is $106,466 per year in Seattle as compared to $125,310 earned by a data scientist working in Boston.
The annual earnings of a data scientist in Seattle is Rs. $106,466 as compared to $88,202 in Washington.
The average annual pay in Seattle for data scientist is $106,466 which is slightly higher than the salary paid to a data scientist in Spokane, which is $97,517.
The average pay for data scientists in Seattle is $106,466 for data scientists, with cities like Bellevue having an average salary of $108,455.
Currently, the demand for data scientist in Washington is quite high owing to the increasing usage of data science in several firms.
The benefits of being a data scientist in Seattle include the multiple job opportunities and tremendous job growth it offers.
Apart from the Salary, being a Data Scientist offers many perks and advantages. Data Scientist is a lucrative job that offers job growth. This job is not bound to a particular field. Today, every major organization, no matter the field, is investing their time and money in Data Science. That not only improves the job opportunities but also gets them in the sight of executives.
Companies like Amazon Web Services, Facebook and Omnidian are hiring Data Scientists in Seattle.
|1.||Data Science Salon | Seattle||October 17, 2019|
TBD Seattle, WA 98101 United States
|2.||The Business of Data Science - Seattle||14 May, 2019 to 15 May, 2019|
Hilton Seattle 1301 6th Avenue Seattle, WA 98101 United States
|3.||Intro to Web Scraping with Python for Data Science||May 2, 2019|
Galvanize Seattle 11, South Jackson Street Seattle, WA 98104 United States
|4.||The Inaugural Sounders FC Analytics Conference||22 June, 2019 to 23 June, 2019||The Ninety 406, Occidental Avenue South Seattle, WA 98104 United States|
|5.||Dataware Symposium Seattle||May 9, 2019||W Seattle 1112 4th Ave Seattle, WA 98101 United States|
|6.||Getting Started in Data Science||May 22, 2019|
The Pioneer Collective 100, South King Street #100 Seattle, WA 98104 United States
|7.||Data Science in Product Design||May 15, 2019|
Product School Seattle 100 S King St #100 Seattle, WA 98104
Data Management in the Geodatabase - July 23-24, 2019
|23 July, 2019 to 24 July, 2019|
King Street Center 201 S. Jackson St. Room: 7289 Computer Training Room (located in the 7th floor elevator lobby) Seattle, WA 98104 United States
|9.||Lunch + Learn : Tiny Data - Intuition in Business||May 17, 2019|
Modern Species 1917 1st Avenue Suite 400 Seattle, WA 98101 United States
|10.||Graph Data Modeling with Neo4j - Seattle, WA||May 15, 2019|
TLG Learning IBM Building 1200 5th Avenue Suite 1565 Seattle, WA 98101 United States
2. The Business of Data Science, Seattle
3. Intro to Web Scraping with Python for Data Science, Seattle
4. The Inaugural Sounders FC Analytics Conference, Seattle
5. Dataware Symposium, Seattle
6. Getting Started in Data Science, Seattle
7. Data Science in Product Design, Seattle
8. Data Management in the Geodatabase, Seattle
9.Lunch + Learn: Tiny Data - Intuition in Business, Seattle
10. Graph Data Modeling with Neo4j, Seattle
|1.||MLconf Seattle: The Machine Learning Conference||May 19, 2017|
AXIS Pioneer aSquare 308, 1st Avenue South, Seattle, WA 98104, USA
|2.||The DRIVE/conference (Data, Reporting, Information, Visualization Exchange)||23 May, 2017 to 24 May, 2017||Hyatt Regency, 900 Bellevue Way NE, Bellevue, WA 98004|
|3.||The Data Science Conference||21 September, 2017 to 22 September, 2017||Hyatt at Olive 8, Seattle, WA 98101, USA|
|4.||TDWI Accelerate, The Fastest Path to Achieving Your Analytics Goals||October 16-18, 2017||Hyatt Regency Bellevue on Seattle’s Eastside 900 Bellevue Way NE Bellevue, WA 98004|
|5.||PASS Summit||October 31, 2017, to November 3, 2017||Washington State Convention Center|
|6.||The 4th AI NEXTCon Conference||January 17, 2018 - January 20, 2018||Meydenbauer Convention Center 11100 NE 6th St, Bellevue, WA 98004|
|7.||2018 INFORMS Regional Analytics Conference||September 14, 2018||Center for Urban Horticulture NHS Hall, 3501 NE 41st Street Seattle, WA 98105|
|8.||7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial-2018)||November 6, 2018||Seattle, WA|
|9.||PASS Summit 2018||November 6, 2018||Seattle, WA|
|10.||2018 IEEE Int. Conf. on Big Data||December 10, 2018, to December 13, 2018December 10, 2018, to December 13, 2018||1900 5th Avenue. Seattle, WA 98101, United States|
1. MLconf Seattle: The Machine Learning Conference, Seattle
2. The DRIVE/conference (Data, Reporting, Information, Visualization Exchange), Seattle
3. The Data Science Conference, Seattle
4. TDWI Accelerate, The Fastest Path to Achieving Your Analytics Goals, Seattle
5. PASS Summit, Seattle
6. The 4th AI NEXTCon Conference, Seattle
7. 2018 INFORMS Regional Analytics Conference, Seattle
8. 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial-2018), Seattle
9. PASS Summit 2018, Seattle
10. 2018 IEEE Int. Conf. on Big Data, Seattle
Here is the logical sequence of steps you should follow to get a job as a Data Scientist.
If you are thinking to apply for a data science job, then follow the below steps to increase your chances of success:
The major roles & responsibilities of a Data Scientist include the following:
The national average salary for a Data Analyst is $95,850 in Seattle, WA. A Data Scientist earns about $120,955 per year.
The career path in the field of Data Science can be explained in the following ways:
Business Intelligence Analyst: A business intelligence analyst develops and provides new business intelligence solutions. They may be tasked with defining, reporting on or otherwise developing new structures for business intelligence in ways that will serve a specific purpose. Report writing can be a significant component of this role. They ensure that the business is always in the best position to utilize its most valuable information in a manner that is conducive to its success.
Data Mining Engineer: A Data Mining Engineer performs the following responsibilities:
Data Architect: The responsibilities of Data Architect is to create database solutions, evaluating requirements, and preparing design reports.
Data Scientist: The chief data officer is a senior executive responsible for the utilization and governance of data across the organization through data management, ensuring data quality and creating data strategy. The various roles include the following:
Senior Data Scientist: The Senior Data Scientist oversees the activities of the junior data scientists and supervises and provides advanced expertise on statistical and mathematical concepts for the broader Data and Analytics department.
Referrals are the most effective way to get hired. Some of the other ways to network with data scientists in Seattle, WA are:
There are several career options for a data scientist –
Some of the tools or software that are preferred over others, following the current trends of recruitment are as follows:
Python is currently among the fastest-growing programming languages in the world. The reasons for it to be a preferred choice are as follows:
The 5 Most popular programming languages commonly used for Data Science are as follows:
Follow these steps to successfully install Python 3 on windows:
Navigate to the Download page for Windows at python.org. Click on the link for the Latest Python 3 Release - Python 3.x.x. Scroll to the bottom and select either Windows x86-64 executable installer for 64-bit or Windows x86 executable installer for 32-bit.
Simply run it by double-clicking on the downloaded file. Then just click Install Now. You must check the box that says Add Python 3.x to PATH as shown to ensure that the interpreter will be placed in your execution path.
There are multiple ways to install Python 3, including a download from the official Python site, but it is recommended instead to use a package manager like Homebrew to manage all your dependencies going forward :
To check if Python 3 is already installed try running the command:
run the following command to install Homebrew
$ xcode-select --install
Click through all the confirmation commands. Install Homebrew:
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
run this command To confirm Homebrew installed correctly:
$ brew doctor
Your system is ready to brew.
run the following command To install the latest version of Python:
$ brew install python3
To confirm the version installed:
$ python3 --version
To open a Python 3 shell from the command line:
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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.
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Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor