Data Science with Python Training in Modesto, 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
  • 100 + 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

data-science-with-python-certification-training

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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Invest in forward-thinking data talent to leverage data’s predictive power, craft smart business strategies, and drive informed decision-making.

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Data Science with Python

What is Data Science

Data science has been called “the sexiest job of the 21st century by Harvard and for all the right reasons. In this day and age when data practically controls every sector and technological advancements have revolutionized the way business is conducted, it hardly comes as a shock to find data science being in such high demand across the world. Data science is a hot favorite among IT graduates in Modesto as well. A small but thriving city, Modesto, CA has seen its fair share of development and is home to leading tech companies, such as QuickBooks Online, Expedition Technologies Inc, Claude Bennett, etc.

Data science is not an easy field, it requires an in-depth understanding of coding, theoretical knowledge and hands-on practical experience. Below are some top technical skills you need to become a data scientist in Modesto, CA -

  1. Python Coding: Python is the simplest and most well-known platform among programmers. Known for its intuitive interface, versatile tools, and advanced features, Python is suitable for beginners and professionals alike. 
  2. R Programming: R Programming has a steeper learning curve when compared to Python. But the platform is great for arranging and analyzing data sets, integrating algorithms and getting insights.   
  3. Hadoop Platform: It is not mandatory to learn Hadoop. The platform is an additional perk that will boost your chances in the industry. It not only allows you to store all forms of data but also provides modules like Pig and Hive for analysis of large scale data.
  4. SQL database and coding: Structured Query Language or SQL is a database language which is used to manage data held in relational database management systems. 
  5. Machine Learning and Artificial Intelligence: Data science, ML and AI go hand in hand. Here are some topics that you must be familiar with as a data science professional:
    • Neural Networks
    • Decision trees
    • Reinforcement learning
    • Logistic regression
    • Adversarial learning
    • Machine learning algorithms, etc.
  6. Apache Spark: Apache Spark is one of the most popular data science platforms for scientists and coders. It creates cache files for its work and is hence a lot faster than other contemporary platforms. It integrates large datasets effectively, helps disseminate data processing and is overall a more convenient option for beginners.
  7. Data Visualization: Data visualization tools allow you to arrange and sort through the unstructured and unfiltered data via platforms like matplotlib and ggplot. It converts complex data sets into a more readable and comprehensible format which makes it easier for data scientists to analyze the given data and get valuable insights from it.
  8. Unstructured data: Data is usually available in a compound, unstructured form. It is neither categorized nor organized into database values. Your job will be to arrange this data, label it accordingly and make it more understandable. 

Being a successful data scientist involves incorporating the following behavioral traits:

  • Ingenuity: A data scientist must be inventive, innovative and creative enough to come up with new and unique solutions to problems. There may be times when you will be facing complicated situations, something that you weren’t prepared for. This requires some out of the box thinking. 
  • Passion: Data science is not an easy field, it requires hours of practice and technical studies. And only a candidate who is passionate enough about his/her work will be able to survive such stressful conditions.  
  • Patience: Data scientists have to be very patient and persevering. This is because your job involves a lot of trial and error, one has to experiment and explore the various algorithms and apply them to the situation.  
  • Creativity: There will be times when one has to develop new queries and algorithms for a particular complicated situation. This requires creativity to come up with new plans and alternatives. 
  • Curiosity: Data science deals with an enormous amount of data every day. A data scientist must be inquisitive and have a never-ending hunger for information. Otherwise, it can get too hard too soon. 

Data science as a career can be quite fruitful. Here are the 5 proven benefits of being a Data Scientist:

  1. Data scientists are among the highest-paid professionals in the industry today. The demand for Data Science jobs is at an all-time high, especially when compared to other career options.
  2. Data science professionals also enjoy a wide scope for exploring different career opportunities. Companies often reward good work with perks like signing bonus, equity shares, and impressive year-end bonuses. 
  3. Data science also requires an in-depth knowledge of coding, programming and academic credentials like a Master’s or a Ph.D. Having a degree opens up alternative career options for people in the academic field as well. You can work as a researcher or a lecturer in a government or a private institution. 
  4. As a data scientist, you will have the opportunity to travel the world. There is a special demand for data science professionals in developed countries around the world. 

Data Scientist Skills and Qualifications

Data scientists are not merely coders or IT professionals. Your job includes the multiple roles of a business analyst, a software engineer, a marketer, a coder, and a good manager. Business skills are hence extremely necessary if you want to be a successful data scientist. It is important to have the following business skills if you want to become a successful data scientist:

  • Analytical Skills: First and foremost, you should be able to analyze a problem and arrive at the crux of the situation quickly and effectively. This requires great analytical abilities and observational skills. 
  • Communication Skills: In data science, communication is critical.  You should be able to communicate deep business and customer analytics to the organization. 
  • Thirst for Knowledge: Data science professionals should have an open mind and a curiosity to learn about the latest trends and new concepts of the industry. 

Data scientists are in demand and the right candidates are rewarded with a future-proofed and lucrative career. Career in data science requires one to be highly knowledgeable, focused and passionate about data and have advanced analytical skills. One has to always be attentive and updated with the latest trends in the industry, keep up with the undercurrents of the market and constantly be ready to absorb and analyze information. Below are the best ways to brush up your data science skills for data scientist jobs:

  • Boot camps: Boot camps are arranged for data science specialists who need to brush up their programming skills. These boot camps also help students garner interest from recruiters through partnerships with businesses.
  • MOOC courses: MOOCs are online courses where one can access lectures and sign up for classes held by data science experts and industry professionals. These classes are live-streamed online and include practical exercises and modules. It is great for tech enthusiasts and beginners. 
  • Certifications: Look for certificate courses online that can add substantial value to your CV. These courses are sponsored by trustworthy institutions and hence are a good investment.
  • Projects: There are online projects and assignments that will be a great addition to your CV. Most data science courses offer project coursework for a practical experience of the industry. 

The Modesto, CA startup ecosystem is ranked 695 globally and is also home to several leading companies, such as Save Mart, Ratto Bros, Central Valley Auto, G3 Enterprises, etc. All these startups and companies are looking for data scientists, who know how to draw valuable insights, which they can leverage to their own advantage.

Data science requires constant practice and mix of theory and technical expertise. Here, we have categorized different problems according to their difficulty level and your expertise level:

  • Beginner Level
    • Iris Data Set: It is applied in the field of pattern recognition and lets you integrate diverse learning methods. If you are a beginner in the field of data science, this dataset is the best for you. This dataset has 4 rows and 50 columns. Practice Problem: The problem is using these parameters to predict the class of the flowers. 
    • Loan Prediction Data Set: This data set is applied to the banking sector and involves a thorough knowledge of the market and its various trends. It is a classification problem dataset with 13 columns and 615 rows. Practice Problem: The problem is to predict if the loan will be approved or not. 
    • Bigmart Sales Data Set: This data set is created for retail, allowing developers a compact and comprehensible way to get market insights, design sales strategies, and advertising campaigns. This dataset is a regression problem with 12 columns and 8523 rows. Practice Problem: The problem is predicting the sales of the retail store. 
  • Intermediate Level:
    • Black Friday Data Set: Collected from a retail store, this dataset, gives the developer an understanding of the everyday shopping experience of customers. It is a regression problem with 12 columns and 550,069 rows. Practice Problem: The problem is predicting the total amount of purchase.
    • Human Activity Recognition Data Set: This dataset applies initial sensors to collect the call log of its customers. It is ideal for the communication and broadcasting industry. It is a collection of 30 human subjects. The dataset consists of 561 columns and 10,299 rows.Practice Problem: The problem is the prediction of the category of human activity. 
    • Text Mining Data Set: The data set is applied to the aviation industry. It is a multi-classification, high dimension problem with 30,438 rows and 21,519 columns. Practice Problem: The problem is the classification of documents based on their labels. 
  • Advanced Level:
    • Urban Sound Classification: This data set applies machine learning notions to everyday problems. Consisting of 10 classes with 8,732, this problem introduces the developer to the audio processing in the real-world scenarios of classification. 
      Practice Problem: The problem is the classification of the sound obtained from specific audio. 
    • Identify the digits data set: This is ideal for the image processing and photography sector. The data set comes with 7000 images of 31 MB and 28X28 dimensions and allows developers to analyze and identify the different aspects of the image. 
      Practice Problem: The problem is identifying the digits present in an image. 
    • Vox Celebrity Data Set: This dataset is used for large scale speaker identification. It uses YouTube videos to extract the words spoken by celebrities. It contains 100,000 words spoken by 1,251 celebrities.
      Practice Problem: The problem is the identification of the voice of a celebrity.

How to Become a Data Scientist in Modesto, California

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

  1. Choose a programming language that you’re comfortable using. R and Python are the most preferred languages 
  2. Now that the programming platform is decided, the next step is to learn about the basics of stats and algebra. In data science, you have to deal with data that can be textual, numerical or image.
  3. Data visualization makes the content clear and comprehensible for the non-technical members of the team. It aids in communication and simplifies the concepts for end-users. 
  4. Every data scientist must have at least a basic understanding of Machine Learning skills for quick and in-depth analysis. 

Here are some effective ways to help you kickstart your career as a data scientist:

  1. Get a degree: Data scientists have more PhDs than any of the other job titles. Apply for a degree or certificate course to get the necessary academic credentials required for the job. 
  2. Hands-on experience: Get some hands-on experience in live projects. Participate in contests, complete projects, and get a data science internship etc. 
  3. Unstructured data: Data scientists have to generally work with unstructured data which has to be labeled, sorted and analyzed. A data scientist is responsible to understand this unstructured data and manipulate it to get optimum results
  4. Programming languages: R and Python are widely used by data miners and data scientists. It is important to know how to code.You need to have the knowledge of programming languages like Python, Perl, C/C++, SQL and Java.
  5. ML and AI: A thorough understanding of ML and AI is also required for data scientists to analyze data and get accurate results from it
  6. Data visualization: With data visualization tools, one can make the data set simpler and accessible even for those who don’t know anything about data science. Data scientists convert this raw data into graphs and charts. There are several tools that can be used for visualization like ggplot2, matplotlib, etc. 

First and foremost, getting a degree in Data Science is vital for candidates. About 88% of data scientists have a master’s degree while about 46% have a Ph.D. degree. Modesto, CA offers students a range of educational institutions where you can apply for data science courses. A degree in data science helps you in; 

  • Networking: It allows beginners to expand their network, make more contacts and even get a chance to interact with experts. This networking will benefit you a lot in the long run as this industry works on referrals. 
  • Structured learning: It offers candidates a structured and comprehensive training of the basic and advanced concepts of data science. This is more beneficial and effective than studying without any planning. 
  • Internship: A degree also qualifies the candidate for an internship at a corporate firm. This internship can be both paid and unpaid. 
  • Recognized qualifications: Last but not least, a degree in data science is a great boost to the CV.

A masters degree is the basic requirement for candidates who want to apply for a job in data science. 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

It is vital that one master the basic programming languages like Python and R. It is the most fundamental skill for anyone in the IT field, irrespective of your location. Below are some reasons why a programming language is required to become a data scientist:

  • Data sets: Data sets form the fundamental aspects of the job. Knowledge of a programming language is a must to analyze this large dataset.
  • Statistics: Analyzing statistics will be an important part of your job. You need the ability to program to implement statistics. 
  • Framework: With coding, you will be able to create a framework that will not only analyze data sets but will also manage the data visualization process. 

Data Scientist Jobs in Modesto, California

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

  • Getting started: Enrol for a degree or certification in Data Science. Next, you need to learn at least one programming language, we would recommend R or Python. 
  • Mathematics: You need to have a good knowledge of mathematics and statistics. Data science involves a lot of charts and tables and graphs as well. Algebra, permutation and combination, probability are some other topics to pay attention to. 
  • Libraries: Get to know about the various processes in data science. Several libraries can be used like Pandas, Matplotlib, SciPy, Scikit-learn, NumPy, ggplot2, etc.
  • Data visualization: Learn to work with raw data and organize it. Find relevant patterns with data visualization tools. The libraries used for this task are ggplot2 and matplotlib.
  • Data preprocessing: The next step is to pre-process the data and engineer it in a more comprehensible format. 
  • ML and Deep learning: You need to have Machine learning and deep learning skills in your CV to get a job as a data scientist.You need to have a tight grasp on topics like CNN, RNN, Neural networks, etc. 
  • Natural Language processing: Natural language processing comprises processing and cataloging textual data. Every data scientist must be skilled in NLP.  

The 5 important steps to prepare for the job as a Data Scientist involves:

  • Study: This involves understanding the technical concepts of data science. Cover all the basic and important topics like statistics, statistical models, probability, neural networks, machine learning, etc. 
  • Meetups and conferences: You need to increase your corporate connections and build your own network. Conferences, tech talks, meet-ups, etc. allow you to do so.  
  • Competitions: After learning the concepts, one needs to implement it as well. Keep practicing, applying, improving, and testing your skills through online competitions like Kaggle. 
  • Referral: According to a survey, the primary source of interviews in companies is a referral. Keep your LinkedIn profile updated. 
  • Interview: The interview is where you finally get to interact with the corporate firm. Be confident and composed in answering the questions asked. Most importantly don't lose hope after a bad interview. Instead, study the questions you weren't able to answer. 

The primary goal of a data scientist is to explore raw data and look for patterns. Then once the pattern is set, he/she has to infer information from it. This data can be present in the form of structured as well as unstructured data. 

Data Scientist Roles & Responsibilities:

  • The first and the most important role of a data scientist is to get the data that is pertinent to the organization. Often you will be given a pile of unstructured data to sort and label through.
  • Next, you have to organize and analyze the data into categories. 
  • Once you have examined the data, you need to generate machine learning techniques to identify patterns in the data and make sense out of it. 
  • Lastly, you need to statistically analyze the data to foresee future results.

A data scientist is expected to fill in several roles, such as a software engineer, a vendor, a trendsetter and a statistician. He has to toil with huge data sets, filter what’s applicable and then gather insights that can be used to predict customer trends as accurately as possible. 

Data Analyst: Data Analyst collects information from various sources and interpret patterns and trends and turns it into information which can offer ways to improve a business. 

Data Scientist: A data scientist is someone who interprets and manages data to help shape or meet business goals. As a data scientist, your job will involve tasks like assessing enormous volumes of data, figure out patterns, develop procedures based on the same. 

Data Engineer: As a data engineer, your job involves assembling data sets, reviewing the business requirements, collaborating with third-parties, creating algorithms and collecting data sets. This information will help developers to estimate market trends as precisely as possible. 

Data Architect: A data architect has to team up with data scientists and engineers to produce elaborate plans for the company. The data architect is responsible for executing the plan. 

Below are the top professional organizations for data scientists in Modesto – 

  • Valley Software Developers
  • Stockton Machine Learning

You can create your network with other Data Scientists through the following:

  • LinkedIn
  • Data Science conferences
  • Meetups

There are several career options for a data scientist in Modesto, CA. These include – 

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

There are some core skills that every company wants. Let’s find out what these skills are:

  • General Skills: General skills are the essential theoretical skills and academic qualifications essential to be a data scientist. Most data scientists have a Ph.D., a degree in Machine Learning and AI and a few research papers. 
  • Technical Skills: Technical skills include a thorough knowledge of programming languages like Python, R Programming, SQL, Hadoop, Spark, JAVA, SAS, Hive, Jsp.net, C++, NSQL, AWL, Scala and more.  
  • Practical Skills: You need to try your hands on real-world projects to improve your skills and build your portfolio. 

Data Science with Python Modesto, California

  1. Python is perhaps the easiest programming platform for a data scientist. It doesn’t matter if you are a newbie or a professional, it is ideally suited for everyone. 
  2. Python is a diverse, extensive and flexible open-source programming language. It runs on the OOPS format and offers a selection of packages and libraries that can be beneficial in the field of Data Science. 
  3. It comes with some of the best analytical and developing tools and data science resources. These tools help you out of the most complex situation and help you find an answer. 
  4. Python has a very far-reaching and intricate community of developers, software engineers and technical experts who are always there to guide you through a tough spot. 

Here are the 5 most popular programming languages used in the Data Science field:

R Programming: R is open-source software, which is used to compute huge data sets, get statistical insights, create customizable graphics, etc. The platform though a bit advanced for beginners is pretty efficient once you figure the core concepts. It includes; 

  • Advanced data packages, statistical models, and easy to edit templates,
  • Connectivity to diverse networks, over 8000 for better visibility
  • Viva GGPLOT, Visual tools for smooth matrix handling  

Python: Python is a handy data tool ideal for examining, positioning and assimilating data into intricate data sets and generating advanced algorithms. It is among the most desirable platforms by data scientists. It is because of the following advantages that it offers:

  • An open-source platform for better elasticity and customization options
  • Comes with special features like Scikit learn, sensor flow and Pandas for quick and effective data analysis 

SQL: SQL or structured query language allows users to assemble data, manage the unstructured data, design relational databases and more. It allows retrieve old data sets, and gain quick and immediate insights. Other benefits include:

  • Versatile, flexible, time-efficient and easy to handle 
  • Great for multitasking 

Java: JAVA runs on the JVM or Java Virtual Machine Platform. It is the preferred platform for nearly every industry. Developers can develop backend systems and applications. Some advantages of using Java are:

  • Java works on OOPS and is compatible with all platforms 
  • Users can edit and design codes for both frontend and backend applications 
  • Plus, it is easy to compile data using Java 

Scala: Scala is based on JVM and hence preferred by data scientists for running huge data sets. The coding interface, powerful tools, and a flexible static tape framework adds on to the platform reliability. Some other benefits are:

  • Scala supports Java and other OOPS platforms 
  • Can be integrated with Apache Spark and other high-performance programming languages. 

Follow these steps to download the latest version of Python 3 on Windows:

  • Download and setup: First and foremost, you have to visit the download page to set up Python on your windows using the GI Installer. Ensure that the pathway is selected in the checkbox, this allows one to decide where the Python 3.x is to be installed.

  • An alternative way is to opt for 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 setup tools and pip: for installing and updating the crucial libraries, you can use the following command:

python -m pip install -U pip

Note: You can create isolated Python environments and pipenv using virtualenv. Pipenv is a Python dependency manager. 

There are two ways by which one can install Python 3 on Mac OS X. You can either install the programming language from the official website using a .dg package. The second method is to pick the Homebrew python version or its alternatives. 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

You should install virtualenv that will generate separate spaces for you to run diverse projects and can even run multiple versions of Python on different projects. 

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.


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.

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What Learners Are Saying

O
Ong Chu Feng Data Analyst
4
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

D
Daiv D Souza Senior Developer
5

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 Front-End Development Bootcamp workshop in July 2021

Z
Zach B Front-End Developer
5

The syllabus and the curriculum gave me all I required and the learn-by-doing approach all through the boot camp was without a doubt a work-like experience! 

Attended Front-End Development Bootcamp workshop in June 2021

E
Emma Smith Front-End Engineer
5

KnowledgrHut’s Front-End Developer Bootcamp helped me acquire all the skills I require. The learn-by-doing method helped me gain work-like experience and helped me work on various projects. 

Attended Front-End Development Bootcamp workshop in May 2021

M
Matt Davis Senior Developer
5

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 Full-Stack Development Bootcamp workshop in May 2021

B
Ben Johnson Developer
5

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

Attended Full-Stack Development Bootcamp workshop in April 2021

M
Merralee Heiland Software Developer.
5

KnowledgeHut is a great platform for beginners as well as experienced professionals who want to get into the data science field. Trainers are well experienced and participants are given detailed ideas and concepts.

Attended PMP® Certification workshop in April 2020

R
Rubetta Pai Front End Developer
5

I am glad to have attended KnowledgeHut's training program. Really I should thank my friend for referring me here. I was impressed with the trainer who explained advanced concepts thoroughly and with relevant examples. Everything was well organized. I would definitely refer some of their courses to my peers as well.

Attended PMP® Certification workshop in May 2020

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