Data Science with Python Training in Fremont, United States

Get hands-on Data Science with 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
  • 100 + Countries and counting

Grow your Data Science Skills with Python

This four-week course is ideal for learning Data Science with Python even for beginners. 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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  • 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


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. Data Science with Python skills will help you to be future-ready.

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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 for the Data Science with Python training program

  • There are no prerequisites to attend the Data Science with Python course.
  • Elementary programming knowledge will be of advantage.

Who should attend the Data Science with Python 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

Python Distribution

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

User-defined functions in Python

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

Datasets and manipulation

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

Probability and Statistics

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

Advanced Statistics

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

Predictive Modelling

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

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

What is Data Science

In 2012, the Harvard Review named Data Science as the sexiest job of the 21st century.  What makes data science such a hot topic?  The answer is simple, data. Data has become an integral part of our lives and it has become difficult to ignore its potential. In a city like Fremont, CA, there are several corporations that are looking for data scientists to help them harness the potential of data including Tailored Brands, Softsol, Astreya, WAY, Snapwiz, Facebook, Trabajo, SLD Laser,, Soraa, Inceptio Technology, SSIT, Central Business Solutions, Quest Groups, Lam Research Corporation, etc. From classifying target audience to improving customer experience across devices and channels, data science offers immense value to businesses.

The top technical skills required to become a data scientist in Fremont, CA, USA, include the following:

  1. Python Coding: One of the most popular languages used in Data Science, Python aids in preprocessing of data and taking different formats of data as input. Its versatility and simplicity gives it an advantage over other programming languages.
  2. R Programming: R programming is another popular language used in the field of data science. It is used as an analytical tool used for solving statistical problems.
  3. Hadoop Platform: Although not a must, knowledge of the Hadoop platform is important for every data scientist. There are several projects in which Hadoop is used. 
  4. SQL database and coding: SQL is used by data scientists for working with data. This involves accessing and communicating with the data. The data scientist must have complete insights about the database they are working on.
  5. Machine Learning and Artificial Intelligence: Machine Learning skills are a must to even be considered for the post of a data scientist. You must be aware of the field of Artificial Intelligence. Make sure that you have an in-depth knowledge of topics like Decision trees, neural networks, adversarial learning, reinforcement learning, logistic regression, etc.
  6. Apache Spark: Like Hadoop, Apache Spark is a data-sharing technology used for performing big data computation. However, it is much faster than Hadoop because unlike Hadoop that reads and writes to the disk, Spark uses the system’s memory to cache its computation.
  7. Data Visualization: This is one of the most important skills a data scientist must have. A data scientist must be able to present the analyzed data in a form that can be understood by the non-technical members of the team. For this, visualization tools like matplotlib, tableau, d3.js, ggplot, etc. are used.

To become a successful data scientist, one must have the following behavioral traits:

  • Curiosity – Since a data scientist has to deal with a lot of data, it is easy to lose interest. So, one must have a hunger for knowledge to make it in the field of data science.
  • Clarity – You must look for clarity at all times. Whether you are cleaning up data or writing code, you need to be clear why you are doing it, what you are doing, and how you are doing it. 
  • Creativity – To look beyond the obvious and find innovative solutions, a data scientist must be creative as well. You must be able to create new innovative tools for analyzing, developing modeling features, and visualize the data.
  • Skepticism – Although creativity is an essential skill, a data scientist must also be skeptical to create a line between a data scientist and a creative mind. They must stay in the real world and not get carried away with creativity.

If you are not sure whether you should study data science or not, here are 5 benefits of becoming a data scientist that will help you decide:

  1. High Pay: Data Scientist Jobs are one of the highest paying jobs in the IT industry today. The average salary for a Data Scientist is $111,748 per year in Fremont, CA.
  2. Good bonuses: When you get hired as a data scientist in a company, you will be able to enjoy signing bonus, equity shares, and bonuses.
  3. Education: A Master's degree or Ph.D. is required to get a job as a data scientist. Once you have that, you can also apply to become a lecturer or researcher in a government or private institution.
  4. Mobility: Data Scientists are in great demand in developed countries. So, getting a job in such a country will not only offer you a handsome salary but also an improved standard of living.
  5. Network: There are several conferences, meetups, and tech talks organized for data scientists that you can use for building your professional network. You will need this in the future for referral purposes.

Data Scientist Skills and Qualifications

If you want to become a top-notch data scientist, you need to have these 4 business skills:

  1. Analytic Problem-Solving – The first thing you do when you have a problem is to analyze it. You need to have a clear understanding and perspective of what the problem demands and work towards a solution accordingly.
  2. Communication Skills – Data Scientists require the right communications skills to bridge the gap between the technical and non-technical members of the team. They need to communicate customer analytics and deep business effectively.
  3. Intellectual Curiosity – A data scientist must always be curious to ask questions like ‘why’ or ‘how’ until you get the right answer.
  4. Industry Knowledge – A data scientist must have a strong knowledge of the industry they are working in. This will help them in cleaning up the data as they will know what needs to be ignored.

If you are thinking of going for an interview to get a job in the field of data science, here are the 5 best ways to brush up your data science skills:

  • Boot camps: Boot camps are the perfect way to get theoretical as well as practical knowledge of data science. They last for about 4-5 days and will help you brush up your data science skills in no time.
  • MOOC courses: These are the online courses where experts from the field of data science will help you brush up your data science skills by giving you assignments to work on your implementation skills. You will also get acquainted with the latest trends in the field of data science.
  • Certifications: Getting certified in a data science course will not only help you work on your skills but also improve your CV. Here are a few data science certifications that you must look into:
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
  • Projects: Work on some projects. These are the best way to brush up on your data science skills as you will be able to work on your implementation skills. You can either explore different solutions to old projects or work on new projects.
  • Competitions: Lastly, you can participate in online competitions that will help you work on your problem-solving skills. Kaggle is one such competition.

There are several companies that have understood the potential of data science and are actively looking for data scientists in Fremont, CA, including Tailored Brands, Softsol, Astreya, WAY, Snapwiz, Facebook, Trabajo, SLD Laser,, Soraa, Inceptio Technology, SSIT, Central Business Solutions, Quest Groups, Lam Research Corporation, Harnham, Tesla, Seagate Technology, Sleep Number, Tokyo Electron America, Ivy Exec, etc. to help them optimize their business processes.

To practice your data science skills with data sets, you can select one of the following problems which are categorized based on your expertise and their difficulty level:

  • Beginner Level
    • Iris Data Set: This is a perfect dataset for a beginner. Containing just 4 columns and 50 rows, the Iris dataset will help you learn about pattern recognition and classification techniques.Practice Problem: Predicting the flower’s class with the help of given parameters.
    • Loan Prediction Data Set: If you want to learn how banking and insurance domain work, this dataset will work for you. It has 13 columns and 615 rows that use data analytics and data science methodologies. This is a classification problem.Practice Problem: Predicting whether the given loan will be approved or not.
  • Intermediate Level:
    • Black Friday Data Set: Containing 12 columns and 550,069 rows, it is a regression problem that consists of daily transactions of millions of customers in a retail store. This dataset will be very effective in helping you explore your engineering skills.
      Practice Problem: Predicting the amount of total purchase.
    • Text Mining Data Set: This dataset contains 30,438 rows and 21,219 columns. It is a high dimensional, multi-classification problem that contains safety reports of problems caused during the flights. It was collected in 2007 in the Siam Text Mining competition.
      Practice Problem: Classifying the documents using the labels.
  • Advanced Level:
    • Identify the digits data set: This dataset contains 7000 images of 82X28 dimensions each. The elements present in the image are studied, analyzed and recognized.
      Practice Problem: Identifying the different elements present in the image.
    • Vox Celebrity Data Set: This dataset is for you if you want to work with audio processing. It contains 1000,000 words, spoken by 1,251 celebrities. Extracted through YouTube videos.
      Practice Problem: Identifying the voice of the celebrity.

How to Become a Data Scientist in Fremont, California

If you want to become a top-notch data scientist, you need to follow the below-mentioned steps:

  1. Getting started: The first step is to select a programming language that you are familiar with. Python and R are the most popular and preferred programming languages used in the field of data science.
  2. Mathematics and statistics: Most of the data that is generated in an unstructured form. This can be text, numbers, images, audios, videos, etc. To decipher a pattern in this data, knowledge of mathematics and statistics is required.
  3. Data visualization: When you are working in a team, you will also have non-technical members on the team. To help them understand the data and get them on the same page, you need to be able to visualize the data. You can use graphs and charts for the same.
  4. ML and Deep Learning: Deep learning and machine learning skills are a must for becoming a data scientist. It is required to build tools used for analyzing the data.

Here are some key steps and skills that are a must if you want to become a successful data scientist:

  1. Degree/certificate: Getting a degree in data science will help you jumpstart your career. You can go for an online or an offline course, depending on what suits your needs the best. During the course, you will learn the fundamentals of data science and how to use the latest tools.
  2. Unstructured data: As a data scientist, you must be able to process unstructured data. Most of the data that we have is in unstructured form, i.e., unorganized and unlabelled. It is not easy to fit this data into a database and requires several complex procedures. You will be able to manipulate the data only after this unstructured data has been processed.
  3. Software and Frameworks: While working in the field of Data Science, you will be required to work with different software, frameworks, and programming languages. Python and R are some of the languages used in data science. You must also be an expert in a framework like Hadoop and Apache Spark. Last but not least, you must know SQL to work with the database.
  4. Machine learning and Deep Learning: Without machine learning and deep learning skills, you won’t be able to get a job as a data scientist. You will need this after the collection and preparation of data, to apply algorithms and analyze the data. To train the model, you will need deep learning skills.
  5. Data visualization: A data scientist is also responsible for helping in data visualization. This is used to help others understand the data as well and make crucial marketing decisions. Tools that can be used for data visualization include matplotlib, ggplot2, etc.

A degree in data science can help you get a job and jumpstart your career. Here is how::

  • Networking – During your course, you will be able to make friends that will help you build your professional network. This can help you get a referral and land a job in the future.
  • Structured learning – If you have trouble in self-learning, getting enrolled in a course will help in structured learning. This is because you will have to follow a strict schedule and study according to the curriculum.
  • Internships – An in-office internship is part of a degree. During the internship, you will get the much-required hands-on experience.
  • Recognized academic qualifications for your résumé – A degree from a reputed institution will put your CV above others and help you land a better job in the data science field.

If you are having trouble deciding if you should get a Master's degree in Data Science or not, here is a scorecard that will help you do so. If your total adds up to more than 6 points, a degree is advised:

  • Strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • Weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
  • Non-STEM background: 5 points
  • < 1 year of experience in Python: 3 points
  • 0 year of experience in regular coding for a job: 3 points
  • Not good at independent learning: 4 points
  • Don’t understand that this scorecard is a regression algorithm: 1 point

Programming language is one of the most important requirements to become a data scientist. Here are the reasons explaining why:

  • Data sets: You will be working with big datasets in data science. Knowledge of programming language is required for analyzing these datasets.
  • Statistics: Statistics is required for analyzing the data, deciphering patterns, and finding relationships in the data. However, just the knowledge of statistics is not enough. You need to have programming skills to implement this knowledge; otherwise, it is of no use.
  • Framework: Programming skills will be required for building a system that will be used for creating frameworks. These will be then used for automating the analysis of experiments, management of data pipeline, and visualization of the data.

Data Scientist Jobs in Fremont, California

If you want to get a job as a data scientist, you need to follow the below-mentioned learning path:

  1. Getting started: The first step is to select the programming language you will be working in. You can choose any language used in data science. However, Python and R are recommended since they are the most popular. You must also be aware of what data science means and what are the roles and responsibilities of a data scientist.
  2. Mathematics: Good command in statistics and mathematics is required to collect the data, decipher patterns, find relationships, and visualize the data. The topics that you must focus on are linear algebra, probability, inferential statistics, and descriptive statistics.
  3. Libraries: Some certain libraries and packages are used for data preprocessing, plotting of structured data, and then use machine learning algorithms to analyze the data. These include, Matplotlib, ggplot2, Pandas, NumPy, SciPy, Scikit-learn, etc. 
  4. Data visualization: Once the data is analyzed, it is the job of a data scientist to visualize it in such a way that is easily understandable for everyone to understand. The most popular ways include charts and graphs. You can use any of the following tools for data visualization:
    • Ggplot2 - R
    • Matplotlib - Python
  5. Data preprocessing: Over 2.5 quintillion bytes of data are created every single day. This data is unlabelled and unorganized. This unstructured data requires preprocessing before it can be injected into the ML tool for analysis. Feature engineering and variable selection are some of the processes required to do this job.
  6. ML and Deep learning: You need to have deep learning and machine learning skills for analyzing the large volumes of the dataset. CNN, RNN, and Neural networks are some of the topics you must focus on.
  7. Natural Language processing: To process and classify the data present in a textual form, you must know about natural language processing. 
  8. Polishing skills: Lastly, you can polish your skills by participating in an online competition like Kaggle. You can also try working on old projects or creating new ones.

If you are preparing for a job as a data scientist, here are 5 important steps that you must follow:

  • Study: Brush up on all the important topics before the interview including the following:
    • Probability
    • Statistics
    • Statistical models
    • Machine Learning
    • Understanding neural networks
  • Meetups and conferences: Start attending data science conferences, tech talks, and meetups to meet other data scientists and build your professional network. This will help you with referrals.
  • Competitions: For testing, implementing and polishing your data science skills, participate in online competitions like Kaggle.
  • Referral: Maintain your LinkedIn profile. This will help you with the referrals, which is the primary source of interviews in the IT sector.
  • Interview: Go for the interview. Doesn’t matter if you can’t get a few questions right. Make sure that you study them and prepare better for the next one.

The job of a data scientist is complex. There are several roles and responsibilities of a data scientist including the following:

  • Collect the data that is required for the analysis.
  • Convert this mostly unstructured data into a structured form.
  • Use machine learning tools, programs, and techniques to make sense of this data.
  • Deliver insights and predict future outcomes after performing statistical analysis on the data.

The average salary for a Data Scientist is $111,748 per year in Fremont, CA 

A data scientist is a part mathematician, computer scientist, and part trend spotter. Here is how the career path of a data scientist goes:

  • Business Intelligence Analyst: The job of a business intelligence analyst is to determine the working of the business and how market trends affect it. They analyze the data to get the exact picture of the current standing of the business.
  • Data Mining Engineer: A data mining engineer’s job is examining the data required by the business. They also create algorithms required for data analysis. Many companies hire them as a third party.
  • Data Architect: A Data Architect works alongside system designers, developers, and users to create blueprints required for data sources’ integration, centralization, maintenance, and protection.
  • Data Scientist: It is the job of a data scientist to analyze the data, develop a hypothesis, and explore the patterns in the given data. They create algorithms and develop data capable of converting raw data into meaningful insights. 
  • Senior Data Scientist: A senior data scientist anticipates the needs of the business in the future. He/she makes sure that these needs are kept in mind while shaping the systems, data analysis process, and all the projects.

The top professional associations and groups for Data Scientists in Fremont, CA are mentioned below – 

  • Big Data Science
  • Data Science and Artificial Intelligence meetup
  • Big Data Application
  • Women in Big Data Meetup

To network with other data scientists, you can try any of the following:

  • Social gatherings like Meetup 
  • An online platform like LinkedIn
  • Data science conference

The top 8 data science career opportunities in 2019 are – 

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

To get employed as a data scientist, you need to have mastery of the following tools and software:

  • Education: You need to either have a Master's degree or a Ph.D. in Data Science to get a job as a data scientist. Getting certifications will also improve your CV.
  • Programming: It is perhaps the most important skill required to be a data scientist. Start with the basics of a programming language and then move on to data science libraries.
  • Machine Learning: Deep learning and machine learning skills are required to become a data scientist. You need to be able to analyze the data and find relationships.
  • Projects: Work on a couple of real-world projects. This will not only improve your implementation skills but also improve your portfolio.

Data Science with Python Fremont, California

Python is considered to be the most preferred and popular language in the field of data science. It is a structured and object-oriented language that offers several libraries and packages that help while working in data science projects. It is simple and easy to learn, read, and understand. There are also several resources available that will help you learn the language and find a way out whenever you are stuck in a problem. 

The 5 most popular programming languages used in the field of data science include:

  • R: Even though it is difficult to learn, it is one of the most popular languages in data science. R has a big, open-source community that offers high-quality, open-source packages. 
  • Python: It is the most popular language used in the field of data science due to the following:
    • Easy to learn, read, understand and implement
    • A big, open-source community
    • Comes with libraries like pandas, scikit-learn, and tensorflow that are used in data science
  • SQL: It is required for dealing with relational databases. It has the following characteristics:
    • The syntax is easy to read, write, and understand
    • Efficient in querying, manipulating, and updating databases
  • Java: The verbosity and a limited number of libraries make Java difficult to use in data science. However, it still offers the following advantages:
    • Since there are already so many systems coded in java, it makes integrating the data science project easier.
    • It is a high-performance, general-purpose, and compiled language.
  • Scala: Even though it has a complex syntax, the following reasons make Scala a preferred language in the data science field:
    • It is compatible with Java as it runs on JVM
    • When used along with Apache Spark, it can perform cluster computing

To download and install Python 3 on Windows, you need to follow these steps:

  • Download and setup: Go to the download page and use a GUI installer to install python on Windows. Check the box that asks to add Python 3.x to PATH allowing the python’s functionalities to work from the terminal.

  • Use the following command to check the python’s version installed on your system:

python --version

  • Update and install setuptools and pip: For installing and updating crucial libraries (3rd party), use the following:

python -m pip install -U pip

To install Python 3 on Mac OS X, follow these steps:

  • Install Xcode: Install the Xcode package of Apple by using the following command: 

$ Xcode-select --install

brew install python

  • Confirm the python’s version installed in the computer using: 

python --version

Data Science with Python Course Curriculum

Download Curriculum

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.


  • 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


  • 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


  • 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


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


  • 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.


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


  • 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
Learning Data Science with Python will help you to understand and execute advanced concepts. Take your advanced statistics and predictive modelling skills to the next level in this 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


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


  • 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.


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


  • 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 applied 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.


  • 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

Our program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the data science with Python course covers everything you need to know, whether you’re a novice or an expert.

Yes, our applied 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. This format is convenient when compared to other Data Science with Python courses.

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 practical Data Science with Python certification course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

Below are the technical skills that you need if you want to become a data scientist.

  • Mathematics - You don't need to have a Ph.D. in math but it is important to have a basic knowledge of linear algebra, algorithms, and statistics.
  • Machine Learning – Stand out from other data scientists by learning ML techniques, such as logistic regression, decision trees, supervised machine learning, etc. These skills will help in solving different data science problems.
  • Coding – In order to analyze the data, the data scientist must know how to manipulate codes. Python is one of the most popular and easy languages.

Other important skills are

  • Software engineering skills (e.g. distributed computing, algorithms and data structures)
  • Data mining
  • Data cleaning and munging
  • Data visualization (e.g. ggplot and d3.js) and reporting techniques
  • Unstructured data techniques
  • R and/or SAS languages
  • SQL databases and database querying languages
  • Big data platforms like Hadoop, Hive, and Pig 
  • Proficiency in Deep Learning Frameworks: TensorFlow, Keras, Pytorch
  • Cloud tools like Amazon S3 

We have listed down all the essential Data Science Skills required for Data Science enthusiasts to start their career in Data Science

Apart from these Data Scientists are also required to have the following business skills:

  • Analytic Problem-Solving – In order to find a solution, it is important to first understand and analyze what the problem is. To do that, a clear perspective and awareness of the right strategies are needed.
  • Communication Skills – Communicating customer analytics or deep business to companies is one of the key responsibilities of data scientists.
  • Intellectual Curiosity -  If you are not curious enough to get an answer to that "why", then data science is not for you. It’s the combination of curiosity and thirst to deliver results that offers great value to a commercial enterprise.
  • Industry Knowledge – Last, but not least, this is perhaps one of the most important skills. Having solid industry knowledge will give you a more clear idea of what needs attention and what needs to be ignored. 

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 Data Science with Python certification 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.

Below is the roadmap to becoming a data scientist:

  • Getting Started: Choose a programming language in which you are comfortable. We suggest Python as a suitable programming language.
  • Mathematics and Statistics: The science in Data Science is all about dealing with the data (maybe numerical, textual or an image), making patterns and relationships between them. You must have a good understanding of basic algebra and statistics.
  • Data Visualization: One of the most important steps in this learning path is the visualization of data. You must 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 to communicate better with the end-users.
  • 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. 

Data Science is one of the emerging fields in terms of its scope to business and job opportunities. Python is one of the most popular programming languages and has become the language of choice for Data Scientists. Learning Python with Data Science puts you in a favourable position to be hired as a skilled data scientist.

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 and we will be happy to get back to you.

We at KnowledgeHut, conduct Data Science with Python courses in all the cities across the globe, and here are a few listed for your reference:



SydneyNoidaBaltimoreNew Jersey
TorontoPuneBostonNew York
OttawaKuala LumpurChicagoSan Diego
BangaloreSingaporeDallasSan Francisco
ChennaiCape TownFremontSan Jose
HyderabadArlingtonLos Angeles

What Learners Are Saying

Ong Chu Feng Data Analyst
The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, Knowledgehut!

Attended Data Science with Python Certification workshop in January 2020

Lea Kirsten Senior Developer

The learning methodology put it all together for me. I ended up attempting projects I’ve never done before and never thought I could. 

Attended Back-End Development Bootcamp workshop in July 2021

Ben Johnson Developer

The Backend boot camp is a great, beginner-friendly program! I started from zero knowledge and learnt everything through the learn-by-doing method. 

Attended Back-End Development Bootcamp workshop in July 2021

Tyler Wilson Full-Stack Expert

The learning system set up everything for me. I wound up working on projects I've never done and never figured I could. 

Attended Back-End Development Bootcamp workshop in April 2021

Bob Cilliers Full Stack Engineer

The curriculum gave me everything I needed and the learn by doing approach throughout the bootcamp was indeed a work-like experience. 

Attended Full-Stack Development Bootcamp workshop in April 2021

Madeline R Developer

I know from first-hand experience that you can go from zero and just get a grasp on everything as you go and start building right away. 

Attended Back-End Development Bootcamp workshop in April 2021

Steffen Grigoletto Senior Database Administrator

Everything was well organized. I would definitely refer their courses to my peers as well. The customer support was very interactive. As a small suggestion to the trainer, it will be better if we have discussions in the end like Q&A sessions.

Attended PMP® Certification workshop in April 2020

Ike Cabilio Web Developer.

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

Attended Certified ScrumMaster (CSM)® workshop in June 2020

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