Data Science with Python Training in San Diego, 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
Group Discount

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.

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

The amount of data that is produced every day is just mind-boggling. By 2020, it's estimated that 1.7MB of data will be created every second for every person on earth. Data-driven decision making is increasing in demand. Data scientists help a company take those important marketing decisions based on data. San Diego, California is a hub for genetics and biotechnology. It is home to several leading companies such as Brain Corp, Patient Safe Solutions, Viasat, Illumina, Mitchell International, Inc. and all these companies are looking for skilled data scientists to analyze their data. 

  • Python Coding: Owing to the versatility as well as the simplicity that Python offers, it takes various formats of data and helps in the processing of this data. 
  • R Programming: Knowledge of R programming is usually an advantage for data scientists in order to make any data science problem easier to solve. It is broadly used for developing statistical software and data analysis.
  • Hadoop Platform: Knowledge of Hadoop isn’t exactly an essential, but very much preferred by the employers. It is needed when the amount of the data exceeds the existing memory of the system. Hadoop is also used while handling data exploration, filtration, sampling, and summarization.
  • SQL database and coding: SQL is a language that is specifically designed to help data scientists access, communicate as well as work on data. 
  • Machine Learning and Artificial Intelligence: Proficiency in the areas of Machine Learning and Artificial Intelligence is now a prerequisite for a career in Data Science. The knowledge and concepts of Machine Learning and Artificial Intelligence that a potential data scientist must be familiar with include the following:
    • Reinforcement Learning
    • Neural Network
    • Adversarial learning 
    • Decision trees
    • Machine Learning algorithms
    • Logistic regression etc.
  • Apache Spark: Apache Spark is a big data computation, not unlike Hadoop. The difference between the two is that Apache Spark is faster, because of the fact that Hadoop reads and writes to the disk, whereas Spark makes caches of its computations in the system memory.
  • Data Visualization: These tools aid a data scientist in the conversion of complex results obtained as a result of processes performed on a data set and convert them into a format that is easy to understand and comprehend. 
  • Unstructured data: It is important for a data scientist to be able to work with unstructured data, which is content that is not labelled and organized into database values. The main responsibility of a data scientist is to proofread, sort, analyze and visualize such data in a structured format.

Below are the behavioral traits employers look for in a Data Scientist in San Diego, CA -

  • Curiosity –  A curious nature is essential to keep your interest in this dynamic field alive. Since you will be dealing with massive amounts of data every single day, you should have an undying hunger for knowledge to keep you going. 
  • Clarity – The field is very vast on its own. Whether cleaning up data or writing code, you should know what you are doing and why you're doing it. 
  • Creativity – You will be required to solve problems and make decisions all day long. For that, you need to be able to figure out what's missing and what needs to be included in order to get results. 
  • Scepticism – Data scientists need scepticism to keep their creativity in check. Scepticism will keep you in the real world rather than letting you get carried away with creativity.

Expect to enjoy the following benefits on the job:

  1. High Pay: Owing to the high demand and low supply, data scientist jobs are one of the highest paying jobs in the IT industry today. The average salary for a Data Scientist is $153,169 per year in San Diego, CA, which is 23% above the national average.
  2. Good bonuses: Although it is a part of their pay, data scientists can expect impressive bonuses. 
  3. Education: By the time you become a data scientist, you would either have a Masters or a PhD. You could well receive offers to work as a lecturer or as a researcher for governmental as well as private institutions.
  4. Mobility: Many businesses that collect data are mostly located in developed countries. Getting a job in one would fetch you a hefty salary as well as raise your standard of living.
  5. Network: Your involvement in the tech world through research papers in international journals, tech talks at conferences and many more platforms would help expand your network of data scientists. 

Data Scientist Skills and Qualifications

Below is a list of the business skills you need to become a data scientist: 

  1. Analytic Problem-Solving – In order to find a solution, it is important to first understand and analyse what the problem is. To do that, a clear perspective and awareness of the right strategies are needed.
  2. Communication Skills – Communicating customer analytics or deep business to companies is one of the key responsibilities of data scientists. Establishing healthy communication helps in working efficiently.
  3. Intellectual Curiosity – This term is the combination of curiosity and the drive to deliver results that produce value for the desired target group. A Data Scientist must possess this skill to excel further in his field.
  4. Industry Knowledge – Last, but not least, this is perhaps one of the most important skills. Having a sound knowledge of the industry you work in gives you the right insight about what to overlook and what is required while working.

Below are the best ways to brush up your data science skills for data scientist jobs:

  • Boot camps: Just like the name suggests, these camps are a kind of crash course that help you get theoretical knowledge and hands-on experience in just a few days. 
  • MOOC courses: These are online courses and include some of the latest trends in the industry. They focus on the implementation skills in the form of assignments.
  • Certifications: Certifications brush up the existing knowledge, give you an additional skill and add up to your CV. Some famous data science certifications are:
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
  • Projects: The more you work on projects, more refined your thinking and skills will be. You can also work on new projects that can refine your skills
  • Competitions: Lastly, competitions like Kaggle etc help improve your problem-solving skills. You need to satisfy all the requirements and find an optimum solution.

San Diego, California is home to many leading companies, such as Brain Corp, Patient Safe Solutions, Viasat, Illumina, Mitchell International, Inc. and many more. It is a fact that every company needs data.  The data science job offered by companies are determined by what kind of companies they are. Small companies use Google Analytics for their analysis - as they have fewer resources and fewer data to work with. Mid-size companies need someone to apply ML techniques on it to leverage it. Big companies already have teams of data scientists, so they would be needing a new data scientist with specialization. For eg: Visualization, ML expert etc.

We have compiled a list of data sets you can practice on, categorized according to their difficulty level for your ease:

  • Beginner Level
    • Iris Data Set: The Iris Data Set is widely accepted to be the most popular, versatile, resourceful and easy data set in the field of pattern recognition. 4 columns and 50 rows.Practice Problem: Use the parameters to predict the class of a flower.
    • Loan Prediction Data Set: The Loan Prediction data set provides the learner with a taste of working with the concepts that are applicable in the domain of banking and insurance. The Loan prediction data set is a classification problem data set. 13 columns and 615 rows.Practice Problem: Predicting if a certain load will be approved or not.
    • Bigmart Sales Data Set: Operations such as Product Bundling, offer customizations, inventory management etc are efficiently handled with the help of this set. The Big Mart Sales Data Set is used in Regression problems. 12 variables and 8523 rows.Practice Problem: Predicting the sales of the store.
  • Intermediate Level:
    • Black Friday Data Set: The Black Friday Data Set comprises of sales transactions that were captured from a retail store. The Black Friday data set is a regression problem.12 columns and 550,069 rows.
      Practice Problem: The problem is predicting the total purchase amount.
    • Human Activity Recognition Data Set: The Human Activity Data Set has a collection of 30 human subjects. 561 columns and 10,299 rows.
      Practice Problem: The problem is predicting the category of human activity.
    • Text Mining Data Set: This data set consists of aviation safety reports that describe the problems that were encountered on certain flights. The Text Mining Data Set is a high dimensional and multi-classification problem. 30,438 and 21,519 columns.
      Practice Problem: The problem is using the labels to classify the documents.
  • Advanced Level:
    • Urban Sound Classification: It is a data set that consists of 8,732 sound clippings of urban sounds that can be categorized in 10 classes. It includes concepts of audio processing in the real-world scenarios of classification.
      Practice Problem: The problem is identifying particular audio and classifying it to its class.
    • Identify the digits data set: This data set comprises of 7000 images, totalling 31MB, with dimensions of 28X28 each. The main aim is to study the picture.
      Practice Problem: The problem is identifying various elements present in the image.
    • Vox Celebrity Data Set: The Vox Celebrity Data Set is meant for large scale speaker identification. It is a collection of words spoken by celebrities and extracted from YouTube videos.
      Practice Problem: The problem is identifying the voice of the celebrity.

How to Become a Data Scientist in San Diego, California

elow are the steps to becoming a successful data scientist in the city of San Diego:

  1. Getting started: Choose a programming language that you’re comfortable with. We recommend Python or R languages. Python provides a more general approach to data science while R is mainly used for statistical analysis.
  2. Mathematics and statistics: The field of data science is all about dealing with the data, making patterns and relationships between them. For this, knowledge of basic algebra and statistics is required.
  3. Data visualization: You have to make it as simple as possible so that the other non-technical teams are able to grasp its contents as well. It is important to learn data visualization in order to communicate better with the end users.
  4. ML and Deep learning: Having deep learning skills to go along with basic ML skills on the CV is a must for every data scientist as it is through deep learning and ML techniques that you will be able to analyze the data given to you.

Here are some key steps and skills that will help you become a successful data scientist:

  1. Degree/certificate: Be it an online or offline classroom course, it is important to start with a basic course that covers the fundamentals. Not only will you learn how to apply cutting-edge tools but also get a boost in your career growth.
  2. Unstructured data: Usually, the data is unstructured and doesn’t fit into a database. Your job is to understand and manipulate this unstructured data.
  3. Software and Frameworks: Due to the huge amount of unstructured data, it is essential that you are comfortable in using some of the most popular and useful software and frameworks to go along with an equally important programming language - preferably R.
  4. Machine learning and Deep Learning: Machine Learning deals with the real-world implications of the theoretical knowledge of the field. It is important to have a basic understanding of both ML and Deep Learning. 
  5. Data visualization: A data scientist’s job is to make sense of huge amount of data given for analysis and provide it to the business in the form of graphs and charts. Some of the tools used for this purpose include matplotlib, ggplot2 etc.

A degree is very important in the field of Data Science because of the following – 

  • Networking – While pursuing the degree, you will get the opportunity to make friends and acquaintances. This will help you land a better job in the future.
  • Structured learning – Following a particular schedule and keeping up with the curriculum is more effective and beneficial than doing things unplanned.
  • Internships – Internships are a great way to get acquainted with an office environment and understand your job better.
  • Recognized academic qualifications– A degree from a prestigious institution will not only look good but will also give you a head start in the race for the top jobs.

Below is a scorecard to help you in making this decision.If your total adds up to more than 6 points, it would be advisable for you to earn a Master’s degree.

  • You have a strong STEM background: 0 point
  • You have a weak STEM background (i.e., biochemistry/biology/ economics or similar degree/diploma): 2 points
  • You are from 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 fare well at independent learning: 4 points
  • You do not understand when we tell you that this scorecard is a regression algorithm: 1 point

Here are the reasons why knowledge of programming language is a must:

  • Data sets: Data science involves working with large amounts of data sets. They are the lifeblood of data science. Understanding and making sense out of them is essential to your job.
  • Statistics: If a data scientist has knowledge about statistics but has no idea how to implement this knowledge, the knowledge of statistics becomes much less useful in his/her application of data science. Without programming language, you won’t be able to apply statistics in Data Science.
  • Framework: This enables a data scientist to build systems that an organization can make use of in order to create frameworks to automatically analyse experiments, visualize data as well as manage the data pipeline at a large organization so that the data can be accessed by the right person at the right time.

Data Scientist Jobs in San Diego, California

Here is the logical sequence of steps you should follow to get a job as a Data Scientist in San Diego, CA.

  • Getting started: Choose a programming language in which you are comfortable. We suggest Python or R language. Make sure to understand the roles and responsibilities of a data scientist.
  • Mathematics: Data science is all about making sense of raw data, finding patterns and relationships between them and finally representing them. Here are a couple of topics that need special attention:
    • Descriptive statistics
    • Probability
    • Linear algebra
    • Inferential statistics
  • Libraries: Data science process involves various tasks ranging from pre-processing the data given to plotting the structured data and finally to applying ML algorithms as well. For this, certain libraries and packages are used. These libraries are Ggplot2, Matplotlib, NumPy, Pandas, Scikit-learn and SciPy
  • Data visualization: It’s your job to make sense of the data given to you by finding patterns and making it as simple as possible. The most popular way to visualize data is by creating a graph. The libraries used for this task are Ggplot2 - R and Matplotlib - Python
  • Data pre-processing: Due to the unstructured form of data, it becomes necessary for data scientists to pre-process this data in order to make it analysis-ready. Pre-processing is done using feature engineering and variable selection. After pre-processing, our data would be in a structured form and ready to be injected into ML tool for analysis.
  • ML and Deep learning: For data analysis, deep learning is highly preferred as deep learning algorithms are designed to work when you have to deal with a huge set of data. It is recommended you spend a few weeks on topics like neural networks as well.
  • Natural Language processing: Every data scientist should be skilled in NLP as it involves processing of text form of data and its classification as well. 
  • Polishing skills: Other than online competitions, you can keep on experimenting and exploring the field by creating your own projects as well.

If you are thinking to apply for a data science job in San Diego, CA, then follow the below steps to improve your chances:

  • Study: To prepare for an interview, cover all the important topics like-
    • Probability
    • Statistics
    • Statistical models
    • Machine Learning
    • Understanding of neural networks
  • Meetups and conferences: Tech meetups and data science conferences are the best way to start building your network or expanding your professional connections.
  • Competitions: They not only brush up your skills but also give you exposure to the field in an interesting and creative way. 
  • Referral: According to a recent survey, referrals are the primary source of interviews in data science companies. Make sure that your LinkedIn profile is well-maintained and updated.
  • Interview: Learn from the questions that you were not able to answer in your last interview and study them for the next interview.

A data scientist is an individual who is responsible for discovering patterns and inferencing information from vast amounts of structured as well as unstructured data, in order to meet the business goals and needs. 

Major Data Scientist Roles & Responsibilities:

  • Fetching data that is relevant to the business.
  • Organize and analyze the data that is extracted from the piles of data.
  • Creation of Machine Learning techniques, programs, and tools in order to make sense of the data. 

The average salary for a Data Scientist is $153,169 per year in San Diego, CA.

https://www.indeed.com/salaries/Data-Scientist-Salaries,-San-Diego-CA 

A career path in the field of Data Science can be explained in the following ways:

Business Intelligence Analyst: A Business Intelligence Analyst does the analysis of data in order to figure out how the business works and how it can be affected by market trends.

Data Mining Engineer: A Data Mining Engineer is an individual who has the job of examining the data for the needs of the business. He needs to create sophisticated algorithms that further aid in the analysis of data.

Data Architect: The role of Data Architect is to work with database administrators and analysts to secure easy access to company data.

Data Scientist: The main responsibility of a Data Scientist is to pursue a business case by analysis, development of hypotheses as well as the development of an understanding of data, so as to explore patterns from the given data.

Senior Data Scientist: A Senior Data Scientist is tasked with the anticipation of business needs in the future and shaping the projects, systems and data analyses of today to suit those business needs in the future.

San Diego, California is growing as one of the hubs of Data Science in USA. Some of the ways to network with fellow data scientists are:

  • Data science conferences held in San Diego
  • Online platform like LinkedIn, job portals and forums
  • Social gatherings like Meetup

There are so many career options for a data scientist in San Diego, California as of today-

  1. Data Scientist
  2. Data Architect
  3. Data Administrator
  4. Data Analyst
  5. Business Analyst
  6. Marketing Analyst
  7. Data/Analytics Manager
  8. Business Intelligence Manager

Here are the tools and software that a data scientist must be an expert in:

  • Education: Data scientists have more PhDs than any of the other job titles. So, getting a degree will be beneficial. Getting certified also adds to it.
  • Programming: You should be good in at least one programming language. Python is the most common as well as easiest of them all.
  • Machine Learning: After preparing the data, deep learning is used to analyze the patterns and find a relationship. Having ML skills is a must.
  • Projects: The best approach to learn data science is by practising with real-world projects so that you can build your portfolio.

Data Science with Python San Diego, California

The simplicity and readability of Python makes it popular among data scientists. It is a multi-paradigm programming language and comes with a broad and diverse range of resources that are available to the data scientist. Python is supported by its big, open-source community.  With many developers working on Python every day, it becomes very easy for a developer to get help in resolving his/her problems.

Below are the most commonly used programming languages in Data Science:

  • R: It may be a bit hard to learn but it has various advantages. It comes with big open-source community that provides R with high-quality open source packages. It also includes a lot of statistical functions that help in carrying out complex matrix operations smoothly
  • Python: Though it has fewer packages than R, python is still one of the most sought after languages in the data science field. Pandas, scikit-learn, and tensorflow provide with most of the libraries needed for data science purpose. It has a big open-source community as well.
  • SQL: SQL is a structured query language which works upon relational databases. It is very efficient when it comes to updating, manipulation, and querying of databases.
  • Java: Even though it has less number of libraries for data science purposes and Java's verbosity limiting its potential, it has many advantages as well. Due to already systems coded in Java at backend, it is easier to integrate java data science projects to it. 

  • Scala: Scala runs on JVM and has a complex syntax to it. Still, it is a preferred language in data science domain. As it runs on JVM, any Scala program can run on Java as well. It can be used for high-performance cluster computing when used along with Apache Spark.

These are the steps to install Python 3 on windows:

  • Download and setup: Go to the download page and setup your python on your windows via GUI installer. While installing, select the checkbox at the bottom asking you to add Python 3.x to PATH, which is your classpath and will allow you to use python’s functionalities from terminal.

  • See if python is installed by running the following command, you will be shown the version installed:

python --version

  • Update and install setuptools and pip: Use below command to install and update 2 of most crucial libraries (3rd party):

python -m pip install -U pip

To install python 3 on Mac OS X, follow the below steps:

  • Install xcode: To install brew, you need Apple’s Xcode package, so start with the following command and follow through it:

$ xcode-select --install

See if it installed correctly: brew doctor

  • Install python 3: To install the latest version of python, use:

brew install python

Check if it’s installed by typing: python --version

Note: Install virtualenv, as it will help you create isolated places to run different projects and may run even on different python versions.

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Meg Gomes casseres

Database Administrator.
Attended PMP® Certification 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 Diego, CA

Its easy to see why this city has self- proclaimed itself as ?America?s Finest City?. Filled with world famous attractions, including monuments and architectural wonders, a bustling economy and locals who are as famous for their sunny dispositions as they are for their sunny beaches, San Diego is a city one can only love. Being one of California?s major cities, it is also the economic centre of the region and hosts several manufacturing, tourism, military, transport, technology, and trade companies including General Atomics, NASSCO, Qualcomm, Nokia, LG, and Websense Inc. It also houses the famous University of California that is famous for its biotechnology related research programs. With world class infrastructure, the university attracts students from all over the globe. KnowledgeHut offers several courses that help you start off 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.