Data Science Course with Python in San Jose, CA, United States

Get hands-on Python skills and accelerate your data science career

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit
  • Create robust predictive models with advanced statistics
  • Leverage hypothesis testing and inferential statistics for sound decision-making
  • 220,000 + Professionals Trained
  • 250 + Workshops every month
  • 70 + Countries and counting

Grow your Data Science skills

This comprehensive hands-on course takes you from the fundamentals of Data Science to an advanced level in weeks. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

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Highlights

  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

Data Scientists are in high demand across industries

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Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need.

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The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

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

Exclusive Post-Training Sessions

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

Prerequisites

Prerequisites for the Data Science with Python training program

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

Who should attend this course?

Professionals in the field of data science

Professionals looking for a robust, structured Python learning program

Professionals working with large datasets

Software or data engineers interested in quantitative analysis

Data analysts, economists, researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

1

Python Distribution

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

2

User-defined functions in Python

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

3

Datasets and manipulation

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

4

Probability and Statistics

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

5

Advanced Statistics

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

6

Predictive Modelling

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

7

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Data Science with Python Course Curriculum

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Learning objectives
Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.


Topics

  • What is Data Science?
  • Data Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools and Technologies 

Learning objectives
The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization

Topics

  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study

Hands-on

  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives
In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value

Topics

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

Hands-on

  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives
Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.

Topics

  • Analysis of Variance (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 for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives
Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression and classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study

Topics

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

Hands-on

  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives
All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

  • Understand Time Series Data and its components - Level Data, Trend Data, and Seasonal Data
  • Work on a real-life Case Study with ARIMA.

Topics

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

Hands-on

  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

Learning objectives
This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.


Hands-on

  • Project to be selected by candidates.

FAQs on the Data Science with Python Course

Data Science with Python Training

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
  • Distribution of data: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Analysis of Variance (ANOVA)
  • Building linear regression models, evaluating model parameters, and measuring performance metrics
  • Using Dimensionality Reduction Technique
  • Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
  • Building KNN algorithm models to find the optimum value of K
  • Building Decision Tree models for both regression and classification problems
  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
  • Build data distribution models: variance, standard deviation, interquartile range
  • Calculate conditional probability via Hypothesis Testing
  • Perform analysis of variance (ANOVA)
  • Build linear regression models, evaluate model parameters, and measure performance metrics
  • Use Dimensionality Reduction
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics
  • Perform K-means Clustering and Hierarchical Clustering
  • Build KNN algorithm models to find the optimum value of K
  • Build Decision Tree models for both regression and classification problems
  • Build data visualization models for Time Series data and components
  • Perform exponential smoothing

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

  • Anyone Interested in the field of data science
  • Anyone looking for a more robust, structured Python learning program
  • Anyone looking to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researcher

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
  • 4 GB RAM
  • 10 GB of free space

Software Requirements

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

  • 32 or 64-bit Operating System
  • 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

Data Science with Python Workshop

The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

Data Science with Python

What is Data Science

Harvard Business Review defined Data Scientist as the “sexiest” job of 20th century in 2012. San Jose is home to many leading companies, such as PayPal, Adobe, eBay, Cisco, Broadcom, Verifone, etc. These companies are looking for expert data scientists to make smart business decisions. 

There are other reasons as well that make Data Scientists hugely popular in today’s world.

  • Decision making based on data is in great demand.
  • There is still a dearth of professionally trained and skilled data scientists in the world. As a result, data scientist is a high paying job.
  • For tech companies around the world, data is king. At the same time, data is being generated at an exceptionally high rate. Understandably, there is an ever increasing demand for data analysts and professionals. Companies are ready are ready to increase the salary and compensation for talented Data Scientists. 

The basic skills required to become a Data Scientist are more or less similar in countries around the world. The skills also vary according to the requirements of the company and the experience requirement. Knowledge and experience in the following are, however, essential:

  1. Python: Python at present is one of the most used programming languages. The basic advantage that it provides is that it has high readability and simple syntax. Also, it can be used for analyzing various forms of data and processing them accordingly. For Data Scientists, Python helps them in developing data sets and also to perform operations on them.
  2. Data Mining: Data mining in simple terms can be defined as knowledge discovery achieved through data integration and warehousing. Since Data Mining is part of Data Science, most major companies nowadays look for Data Scientist having optimal data mining skills.
  3. Machine Learning Algorithms: Machine Learning Algorithms are used to predict and categorize data sets. It also helps in prioritizing data. It forms an essential part of data processing, which makes it important for data scientists to be skilled in the same. 
  4. SQL: Structured Query Language (SQL) is helpful for data scientists to communicate and work with various data sets. It is also used by Data Scientists to understand the structures and formation of data. MySQL helps in improving productivity and also helps decrease the amount of time required to carry out some of the functions. 
  5. Data/ Statistical Analytical tools like SAS: SAS or Statistical Analysis system is another programming language that is used commonly by Data Scientists. Learning SAS is particularly important for people who want to work for big multinational corporations, since a majority of these companies rely on SAS’ effective customer service. SAS is also important for Data Science professionals looking at career opportunities in the financial technology sector.
  6. R programming: R programming has gained popularity over the years for companies as well. It is generally used for statistical analysis and has a huge community to support its users. Data Scientists as such, would immensely benefit from R programming as most of the start-ups at present favour R programming over other languages.
  7. Hadoop: Hadoop may not be technically related to Data Science, but most of the Data Science projects at present use it. A LinkedIn survey concluded that Hadoop skill is one of the most important ones for Data Scientists.
  8. Data Visualization: As a Data Scientist, you should be able to visualize data by using tools like d3.js, matplotlib and others. The main purpose of data visualization is to help in data processing and transforming data into formats that are easily understandable.

Most companies define their own job profiles and characteristics that they are looking for in a Data Scientist. However, there are some basic traits that a Data Scientist should possess:

  1. Being Curious: Dealing with huge number of data can be immensely stressful. Hence, you need to be curious about finding insights from data and answering questions.
  2. Being Clear: This means that you should be clear about your goals and also about your data. While writing code for programs, you need to understand why you are doing what you are doing.
  3. Being Creative: Being creative would involve you to be able to think out of the box and work in innovative ways to find solutions to problems. This can include newer ways for visualization of data, developing new tools or others. You should be able to find out the problem if there is any and modify the process accordingly.
  4. Being Skeptical: This should allow you not to go overboard with your creativity, and be grounded. Often times, your creativity can lead you to develop solutions that are not feasible in the real world. Being skeptical will help you check your creativity.

The Harvard Business Review considered it as the “sexiest job of the 21st century”. Understandably, there are some major benefits that you can earn as a Data Scientist. Some of them have been explained below:

High Salary: Companies at present are willing to shell out higher salaries for experts in data science. This is because of the demands in the market for Data Scientists and the nature of the job, itself. Data Scientists need to be proficient in at least two programming languages, understand data and financial analysis, consider the consequences of their decisions based on data analysis and then support companies’ marketing decisions. As the demands from this profile are high, so are the salaries.

Bonus:  Apart from the high salaries that companies pay to Data Scientists, there are also bonuses that the employee can get. Major companies also offer equity shares to Data Scientists.

Academics: In order to become a Data Scientist, one must be strong academically. Companies usually hire those who have a Masters or a PhD in the field. Hence, when you finally bag a lucrative job, be it as a professional in a major company or a researcher in universities, you have acquired a fair amount of knowledge in the field.

Lifestyle: Data Scientist jobs are mostly offered in places that are well-connected and developed. So, apart from the high salary that you will be drawing, your standard of living will also improve to a great extent.

Networking: As a Data Scientist, you will be able to come in contact with a lot of experts. You might also get invited to tech talks, which is likely to expand your social as well as professional network.

Skills and Qualifications of a Data Scientist

As a Data Scientist, you need to have the following skills

  • Analytical mindset: As a Data Scientist, you need to be able to find solutions to problems. Therefore, a clear mind that is able to analyze problems is an important skill that a Data Scientist needs to have.
  • Excellent communication skills: Communicating problems and solutions to clients and colleagues is an important skill for a Data Scientist. 
  • Curiosity: In order to come up with solutions that are innovative and at the same time practical, a Data Scientist needs to be curious. 
  • Knowledge about the Industry: Since as an industry, Data Science is continuously evolving, you need to keep up with the latest industry knowledge and develop your skills accordingly. This will work only if you are curious enough to apply the knowledge to your profession.

As a Data Scientist, you can improve your Data Science skills by being involved with any or all of the following ways-

Boot Camps: Boot Camps are an excellent way to help you practice your Python skills. These camps are usually 4-5 days long and are effective if you want to improve both your practical as well as theoretical knowledge.

Massive Open Online Courses: These Online Courses are delivered by experts in Data Science. You will also get the chance to stay updated about the latest trends and practice your skills on assignments.

Getting certified: Getting certified will help you with improving your skill sets. Also, certifications will add credibility to your CV when you start applying for jobs. The following are some of the most important Data Science certifications that you can get:

  • The Master of Science in Data Analytics at San Jose State University
  • Data Science with Python Foundation Training
  • Data Science Fellowship at the University of San Jose

Taking part in competitions: Taking part in competitions will enhance your capabilities to work with limited constraints and also work effectively to find out the solutions to the problems.

Data is everywhere in today’s world. Every form of information that you need or will possibly need starting from your investment details and your medical bills to your phone number and residence address is data. Companies use this data for their marketing purposes and enhance the customer experience that they offer you. Verifone, Fair Isaac Corporation, Cisco, PayPal, Adobe, eBay, Broadcom,  etc. are some of the companies that are hiring in San Jose.

The best way to master anything is certainly to practice it over time. This applies to Data Science as well. In order to solve Data Science problems, you will certainly have to work on various aspects of Data Sets and understand what might work for you. Here, we have categorized different problems according to their difficulty level and your expertise level:

  • Beginner:
    • Iris Data Sets: These are generally the most popular and easy to work with among data sets in pattern recognition. This is also easier to learn when you are trying to pick up classification techniques. This dataset has just 4 rows and 50 columns. Problem to practice: Predicting a flower class based on some specifications.
    • Loan Prediction Data Sets: Banking and finance industry uses Data Science to a huge extent. The Loan Prediction Data Sets, hence, are particularly useful for individuals who want to gain entry into the industry as a Data Scientist. These Data Sets also, help the beginner to understand the various aspects of banking and finance like the variables that are used or the strategies that need to be implemented for a problem. There are 13 columns and 615 rows in Loan Prediction Data Set problem.Problem to practice: The problem is to predict if the loan will be approved or not.
    • Bigmart Sales Data Set: This data set is useful in the Retail Industry. Considered as one of the largest industries to make use of Data Analytics, the Retail industry requires Data Science and Data Analytics in order to provide Product Bundling and customization of offers. Also, inventory management is easier to perform through Data Sets. This dataset is a regression problem with 12 columns and 8523 rows.Problem to Practice: Predicting the retail store sales.
  • Intermediate:
    • Black Friday Data Set: This is generally helpful for learners to practice on transactions in a retail store. The Data Set helps with developing engineering skills and to understand how millions of customers shop throughout the day. It has 12 columns and 550069 rows.
      Problem to practice: Predicting the number of purchases made by customers.
    • Human Activity Recognition Data Set: This dataset is collected using the recordings of smartphones collected using inertial sensors. It is a collection of 30 human subjects. The dataset consists of 561 columns and 10,299 rows.
      Problem to practice: Predicting the category of human activity.
    • Text Mining Data Set: This Data Set is generally used for aviation safety reports. It contains problems that have been encountered by flights. There are 30438 rows and 21519 columns in Text Mining Data Set.
      Problem to practice: Classifying documents based on categories.
  • Advanced:
    • Urban Sound Classification: It introduces and implements machine learning concepts to real-world problems. Consisting of 10 classes with 8,732 sound clippings of urban sounds, this problem introduces the developer to the audio processing in the real-world scenarios of classification. 
      Problem to practice: The problem is the classification of the sound obtained from specific audio. 
    • Identifying digits: Consisting of 7000 images of 31 MB and 28X28 dimensions, this data set helps you in studying, analyzing, and recognizing elements present in a particular image. 
      Problem to practice: The problem is identifying the digits present in an image.
    • Vox Celebrity Data Set: This is another form of audio processing, through which the learners are required to identify the voice of celebrity. This data set, at present, has 100000 voices recorded from 1251 celebrities from all around the world. These voices are extracted from YouTube videos.
      Problem to practice: Identifying the voice of a celebrity.

How do I become a Data Scientist in San Jose, California

Below are the steps that you must follow in order to become a top-notch Data Scientist:

Starting out: The first step involves choosing the right programming language. Python or R are most commonly used languages. 

Learning Mathematics and Statistics: These form the basis of your knowledge. As a Data Scientist, you will have to go over various forms of data including numbers, texts and images, and analyze them by seeking out patterns. You need to have a basic understanding of statistics and algebra.

Visualizing Data: Data Scientists also need to work in teams, and for you to be a good Data Scientist, communication will be crucial. Visualizing Data involves analyzing data and communicating the information to your non-technical peers in a way that they understand. 

Machine Learning and Deep Learning: For every data scientist, it is a must to have basic Machine Learning skills along with deep learning skills in their CV. With these, you will be able to analyze any data given to you.

In order to ease your path to become a Data Scientist, we have listed some of the steps and key skills required to help you kickstart your career as a data scientist:

  • Earning a degree or a certificate: This is crucial as the job market in Data Science is particularly complex. A major company would generally look for a Master degree if not a PhD from an applicant in Data Science. Moreover, a degree and certificate will add credibility to your CV.
  • Understanding Unstructured Data: The fundamental work of a Data Scientist is to go through the volume of unstructured data and manipulate it to get optimum results. 
  • Learning about Software and Frameworks: Learning how to categorize unstructured data should be accompanied with working on various software and popular frameworks. You also need to learn a programming language to go along with the framework. The most preferred language in Data Science is Python and R. 
    • R has a steep learning curve, which really does not make it an easy programming language to learn. But it is one of the most used Programming Languages. Nearly 43% of Data Scientists use R programming language to analyze their data.
    • Hadoop as a framework is useful for Data Scientists to handle excess amount of data. In case the volume of data goes over the given memory at hand, Hadoop is used. Spark is a similar framework used in such situations as well. Spark provides a few more advantages over Hadoop like faster computational work and prevention of data loss.
    • Apart from the software and framework, it is required from a Data Scientist that they are expert in knowledge databases, as well. As such, they need to be skilled in SQL queries.
  • Understanding Machine and Deep Learning: After data has been prepared, it is important for you as a Data Scientist to be able to apply algorithms to ease out analysis. We can train our model using deep learning techniques and analyze the data. 
  • Understanding Data visualization: Data visualization involves communicating the analyzed data to non-technical colleagues in the form of graphs and charts. This is critical as communicating the same to them will lead to a better team effort. The general tools that are used for such purposes are matplotlib and ggplot among others. 

At least 46% of Data Scientists hold a PhD degree, so, it is important that you continue with higher studies after Graduation to land a lucrative job. Below are some other benefits of getting a degree:

Helps with networking: Throughout your entire journey as a student and learner, you will get to meet and work with people who share the same kind of interests. This is going to help you immensely in the future when you will start working as a Data Scientist.

Being organized about learning: When you are pursuing a degree, you will have to keep up with the curriculum and follow a particular schedule. This is more beneficial and effective than studying without any planning.

Opportunities for Internships: Degrees will help you find the appropriate internship opportunity, which in turn, will help you get a job. You will also gain practical knowledge through the same.

Earning credibility: Lastly, earning a degree is not only about the knowledge that you gain as a student, but also is about the credibility that it adds to your CV. You will have something to show and speak for your expertise and skill, if you have a degree from a reputed institute.

If you are having trouble in deciding whether you should go for a Master’s degree, you can try grading yourself on the basis of the below scorecard. If your score is more than 6 points, you should get a Master’s degree:

  • A strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • A weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
  • A non-STEM background: 5 points
  • Less than 1 year of experience in Python: 3 points
  • No experience of a job that requires regular coding: 3 points
  • Independent learning is not your cup of tea: 4 points
  • Cannot understand that this scorecard is a regression algorithm: 1 point

Having programming knowledge is one of the most critical aspects of being a Data Scientist. The main reasons are as follows:

Helps with working on Data Sets: As a Data Scientist, you will be required to work with huge amount of data. Programming knowledge will help you with such large data sets analysis.

Helps with Statistical application: A data scientist has to work with statistics. You need the ability to program to implement statistics. Without the knowledge of programming language, knowledge of statistics does not do much good. 

Helping out with Framework: Programming knowledge is also useful when trying to apply data science in the most effective way possible. Developing Frameworks becomes easier which, in turn, can be used to make sure that the right data is accessible.

Jobs for Data Scientists in San Jose, California

If you want to get a job in the field of Data Science, you need to follow this path:

Starting out: Learn a programming language that you will be comfortable with. The most commonly used programming languages in Data Science are Python and R language.

Learning Mathematics: This is critical, since, as a Data Scientist, you will be working with raw data. Having a strong hold on Mathematics and Statistics will be helpful. You need to pay special attention to Descriptive statistics, Probability, Inferential Statistics to further your knowledge.

Understanding Libraries: This is important to perform tasks like data processing and for structured data plotting. Some of the most common libraries are SciPy, ggplot, Matplotlib and others.

Understanding Data visualization: Another important aspect of a Data Scientist job is to find patterns in unstructured data and to communicate the same to people who are from non-technical backgrounds. Therefore, data visualization becomes important. The libraries used for this task are ggplot2 and matplotlib. 

Understanding data pre-processing: The unstructured nature of data makes it important for data scientists to pre-process the data before making it ready for analysis. This is usually done through feature engineering and variable selection. 

Machine Learning and Deep Learning: Deep learning algorithms are used while dealing with a huge set of data. You need to have a tight grasp on topics like CNN, RNN, Neural networks, etc. 

Learning Natural Language processing: This is important to understand how the text form of data can be processed and classified.

When preparing for a Data Scientist Job, you will need to go through the following steps to be able to increase your chances.

  1. Learn: Cover all the basic and important topics like including Statistics, Statistical Models, Probability, Machine Learning, and Neural Networks.
  2. Take part in conferences and technology gatherings: Build your network and develop connections by participating in conferences and meetups.
  3. Take part in competitions: You need to keep practicing, implementing, polishing, and testing your skills through online competitions like Kaggle. 
  4. Referrals: Referrals are a great way to get a good job. Keep your LinkedIn profile updated.
  5. Final step: If you feel that you are ready for the interview, go for it. 

The main role of a Data Scientist is to make sense of the huge amount of data that is being generated on a daily basis and make it business ready. First, you need to get the data that is relevant to the business from the huge amount of data provided to you. This data can be in structured as well as unstructured form. Next, this data needs to be organised and analysed. After this, you need to create machine learning techniques, tools and programs to identify patterns in the data and make sense out of it. Lastly, you need to perform statistical analysis on the data to predict future outcomes.

Salaries of Data Scientists depend on the type of company and the job profile. Depending on the roles and responsibilities of a Data scientist, the average pay scale is as follows:

  • Data Scientist: $122,338/yr
  • Data Analyst: $124,826/yr

https://www.indeed.com/career/data-scientist/salaries/San-Jose--CA

https://www.paysa.com/salaries/data-analyst--san-jose,-ca--tl

A Data Scientist will work with huge volumes of data and predict the outcome based on the same. The whole career path of a Data Scientist can be explained as follows:

  • Business Intelligence Analyst: The role entails performing the analysis of the data provided by the organization.
  • Data Mining Engineer: The role of data mining engineer is to create and enhance statistical and predictive models and algorithms to analyse very large data sets. 
  • Data Architect: This role involves working with system designers to develop data management systems blueprints which can be used to maintain, integrate and centralize the data sources.
  • Data Scientist: A Data Scientist has the responsibility of doing the analysis, pursuing a business case, developing hypotheses, understanding the data, and exploring patterns from the provided data. 
  • Senior Data Scientist: A Senior Data Scientist is one who anticipates the future needs of the business and adopts the best practices within the data science department for modelling as well as statistical analysis. 
  • Data Science: Pleasanton Dublin San Ramon Danville Livermore
  • Data Science Dojo – Silicon Valley
  • Sunnyvale Women in Data
  • Silicon Valley Data Science, ML, AI Platform
  • Women in Big Data Meetup
  • Bay Area Data Science Enthusiasts

The most effective way is to go through referrals. Other ways to hire Data Scientists for the team include Data Science Conferences, LinkedIn and Social Meetups.

As of 2019, a Data Scientist can look at the following career opportunities:

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

Employers generally prefer data scientists to have mastery over some software and tools. They generally look for:

  • Education: Getting a degree in Data Science, like a Master's degree or a Ph.D., will help you in the long run.
  • Programming knowledge: Programming is one of the most important skills required to be a data scientist. You can try Python or R programming language.
  • Machine Learning: Once you have collected the data and converted it into a structured form, you will need deep learning and machine learning skills to find relationships and analyze patterns. 
  • Working on prior projects: Try real-world projects to improve your skills and build your portfolio.

Python for Data Science San Jose, California

Python is one of the most commonly preferred languages used by Data Scientists because of its simplicity and readability. It is an object-oriented, structured programming language that comes with several packages and libraries that can be beneficial in the field of Data Science. The other benefit of using Python as a programming language in Data science is the vast community dedicated to the language. 

Following are some of the most popular programming languages commonly used for Data Science: 

  • R: R is generally avoided by beginners as it has a steep learning curve. However, it still has a few advantages that make it useful for professionals.
    • It has a huge online, open-source community.
    • It is capable of handling complex matrix equation while dealing with loads of statistical functions smoothly.
    • R uses ggplot2 to provide a good data visualization tool.
  • Python: It is one of the most commonly used programming languages in the field of data science even though it has fewer packages than R. It is because of the following advantages that it offers:
    • It very easy to learn, understand and implement.
    • It has the support of a big open-source community as well.
    • It has most of the libraries that you might need for data science like scikit-learn, tensorflow, and Pandas.
  • SQL: Structure Query Language is used for working with relational databases. Some of its benefits are:
    • It has a readable syntax
    • It is very efficient in manipulating, updating, and querying relational databases.
  • Java: It is a general purpose, high-performance, and a compiled language. There are several systems that are already coded in Java at the backend. This makes the integration of data science projects to these systems easy. 
  • Scala: Scala has a complex syntax but it is quite favoured by Data Scientists because of the following reasons:
    • Scala runs on JVM that makes it compatible with Java as well.
    • Scala also ensures high-performance cluster computing, when it is used along with Apache Spark.

Here is how you can download and install Python 3 on Windows:

  • Download and setup: Visit the download page and use the GUI installer to setup Python on your windows. Make sure that while you are installing, you select the checkbox asking to add Python 3.x to PATH. 

  • You can also use Anaconda to install Python. If you want to check if Python is installed, you can try using the following command that will show the current version of Python installed:

python --version

  • Update and install setuptools and pip: If you want to install and update the crucial libraries, you can use the following command:

python -m pip install -U pip

For installing Python 3 on Mac OS X, you can either simply install the language from their official website using a .dg package or use Homebrew python or its dependencies. Here are the steps you need to follow:

  • Install Xcode: First, you need to install Xcode. You will need the Xcode package of Apple/ Start using the following command: $ xcode-select --install
  • Install brew: Next, you have to install Homebrew which is a package manager for Apple. Start with the following command: 

 /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" 

Confirm if it is installed by typing: brew doctor

  • Install python 3: Lastly, to install python, use the following command: 

brew install python

If you want to confirm the version of python, use the command: python --version

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Data Science with Python Certification Course in San Jose, CA

A major farming town in the 1800?s San Jose has today transformed itself into a technology giant prompting the nick name "Capital of Silicon Valley?. It has the largest number of high-technology engineering, computer, and microprocessor companies including Adobe, Cisco, eBay, Hitachi, Netgear and many more. The internet boom catapulted the income of San Jose and made it among the richest areas in California with a high cost of living. But the city is not all about technology and silicon chips. It is a thriving centre for arts and culture and home to several performing arts companies and hosts the San Jose Jazz Festival and San Jose Asian American Film Festival each year. This bustling city is a right place to start a career and KnowledgeHut helps you along the way by offering courses such as PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses, Big Data Analysis, Apache Hadoop, SAFe Practitioner, Agile User Stories, CASQ, CMMI-DEV and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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