# Data Science with Python Certification Course

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You will learn key Python programming topics like information transmission, directory processes, object-oriented programming, and numerous Python frameworks necessary for data science in the Data Science with Python program. Both learners and specialists would benefit from taking this course. Their future in data science will get off to a high note thanks to this course’s introduction to various machine learning models, reinforcement learning, and other pertinent ideas.

The widely used software pandas data science module will be used to teach students how to manipulate and clean information, explain the abstraction of Serial and Data structure as the key data models for data processing, and provide instructions on how to efficiently use the tool. Students will be prepared to take statistics, help clean up, modify it, and perform fundamental interpretive data analysis by the conclusion of the course.

Key Features

Course Duration : 4 months

Real-Time Projects : 2

Instructor-Led Training

EMI Option Available

Certification & Job Assistance

24 x 7 Lifetime Support

Our Industry Expert Trainer

We are a team of 10+ Years of Industry Experienced Trainers, who conduct the training with real-time scenarios.

The Global Certified Trainers are Excellent in knowledge and highly professionals.

The Trainers follow the Project-Based Learning Method in the Interactive sessions.

## data science with python course Overview

**Data science with Python training** at 3RI Technologies will cover subjects like data transmission, directory procedures, object-oriented programming, and several Python frameworks required for the **data science with python course.** Both novices and experts would benefit from taking this course. This course will give them a head start in data science by introducing them to various machine learning models, reinforcement learning, and other crucial concepts.

Students will learn how to manage and clean data, understand the abstraction of Serial and Data structure as the essential data models for data processing, and learn how to use the widely used programme pandas in the data science curriculum. Students will be able to take statistics, assist in cleaning them up, altering them, and performing rudimentary interpretive data analysis by the end of the course.

After taking the **data science and Python course** at 3RI Technologies, you will be an expert in data science. During the online course in Data Science, we’ll work on several projects and case studies. This kind of project is what makes our course different from others. As you work on this project, you’ll see how a product is made, put into use, and tested. You will learn the best ways to do things, which can help you get the job you want. So, **data science with python certification** would brighten your future to secure a job in the IT industry.

**Data Science Machine Learning**

Data science and machine learning are ideas in the field of technology. They use data to help us create and improve products, services, infrastructure systems, and other things. Both are good choices for jobs that are in demand and pay well.

They have a similar relationship: squares are rectangles, but rectangles are not squares. Data science is the rectangle that includes everything, and machine learning is the square that makes up its own thing. Data scientists often use them, and almost every industry is moving quickly to adopt them. Because of this, more and more people are taking** data science and machine learning courses.**

**What is Machine Learning?**

The artificial intelligence subfield known as machine learning (ML) helps computer programs improve their predictive abilities over time without being explicitly taught how to do so by humans. Predicting future values is the main purpose of machine learning algorithms, and they do so by analyzing past data.

Recommendation engines are one popular area where machine learning is put to use. The automation of business procedures, predicting mechanical failures, and detecting fraudulent emails are examples of popular uses.

**What Makes Machine Learning So Crucial?**

Machine learning is crucial because it allows companies to recognize trends in consumer behavior and transactional data. In addition, it encourages the development of brand-new items. Because it can process data far more quickly and comprehensively than the human brain, ML has proven to be a valuable tool. Machines can be taught to detect patterns and links in the data they are given and to automate activities that humans typically undertake with the help of a lot of processing power behind a single work or multiple critical duties. This is why **machine learning training **has spread so rapidly around the globe.

**How Does Machine Learning Function?**

Machine learning learns about and interprets the world using input like training data or knowledge graphs, much like how the human brain absorbs new information and builds on it. Deep learning can start once a taxonomy of entities has been established.

A machine must initially gather and interpret information before it can begin to learn. This information may come from examples, firsthand knowledge, or explicit instructions. Information is analyzed for recurrent structures to draw conclusions from the examples given. Making it possible for robots to learn and adapt on their own without human assistance is machine learning’s ultimate goal.

**What Are The Various Kinds of Machine Learning?**

There are four fundamental strategies: semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning.

**Supervised Learning**– In this kind of machine learning, data scientists give algorithms training data that has been labeled and tell the algorithm which variables it should look for correlations between. Both what goes into the algorithm and what comes out are given.

**Unsupervised Learning –**In this kind of machine learning, algorithms train on data that has not been labeled. The algorithm looks at each set of data to see if there is any connection that makes sense. In other words, the training data that algorithms employ and the results of their predictions and recommendations are unchangeable.**Semi-Supervised Learning**– This approach combines these two machine learning types. Data scientists may feed an algorithm mostly labeled training data. However, the algorithm can independently research the data and draw inferences about the data set.**Reinforcement Learning –**In this method, these two types of machine learning are put together. Data scientists may feed mostly labeled training data to an algorithm to help it learn. Still, the algorithm can look at the information independently and determine its meaning.

The partnership between Machine Learning and Python solidified its IT and data science position. Several market leaders use Python for many tasks, such as web development, process automation, software application development, etc. StackOverflow says that Python’s popularity will grow greatly in the next few years.

The key justifications for using Python and data science in machine learning are listed below:

**Independence of Platform**

Python is a programming language that can run on various operating systems and software architectures since it is essentially platform-independent. The developer can write the code, compile it, and execute it across various platforms.

Python is well known for its extreme adaptability and can be used on various operating systems, including Windows, Macintosh, Linux, Solaris, macOS, and more. It is simple to integrate Python with other languages, such as Java,.NET, C/C++, Perl, PHP, R, etc.

**Excellent Data Visualisation**

Data presentation is crucial in data science and machine learning. Python has been quite useful in presenting data in a format understandable by humans. Great data visualization tools are provided by Python libraries like MatplotIib, which make it easy to set up the data, parameters, figures, and charts. Line plots, histograms, contouring and pseudocolor, pictures, three-dimensional plotting, pathways, and many subplots are just a few of the ways these libraries assist in presenting data. Therefore, a **machine learning certification course** requires python with data science abilities.

**What Will You Learn in This Machine Learning Course?**

The Data Science & Machine Learning Bootcamp is a thorough machine learning certification course designed for students who wish to understand machine learning fundamentals, including regression, classification, and neural networks.

The **Data science machine learning course** will comprehensively introduce the practical aspects of developing machine learning algorithms and teach you how to utilize standard tools such as Tensorflow.

The comprehensive Data Science & Machine Learning Bootcamp teaches newcomers what they need to know to get started in data science and Python programming. You will begin with an overview of machine learning, followed by constructing neural networks that will be used in deep learning applications. You will also comprehensively understand other tools, including Matplotlib and NumPy.

This machine learning course would last for four months and provide you with an in-depth look at the various things that can be accomplished with ML techniques. Included among the topics discussed are:

- Python for machine learning and data science programming

- Progression linearity for predictive models

- How to implement neural networks and trained models

- Using Tensorflow to recognize handwritten digits

- Developing one’s projects for the ML portfolio.

**Key Features**

- Live Projects
- Placement Assistance
- Real-time Project Experience on AWS Console
- Flexible Timings
- Certified Course
- Interactive Sessions
- 24/7 Support & Access
- Additional exams, tests, and mock conducted

Duration:** 40**** Hours**

A job-focused, short-term course on data science with Python is a wise investment in your professional development. With data being the primary source of decision-making in almost every business, these programmes provide a quick and efficient way to get the skills required to succeed in the highly competitive field of data science and analytics.

** **

First and foremost, the goal of these courses is to provide students with relevant industry-ready skills in a short amount of time—usually a few weeks to a few months. This implies you can quickly pick up the skills and information needed to succeed in data science positions. Fundamental ideas in data science, Python programming, data cleansing and collecting, exploratory data analysis, and machine learning are frequently covered in the curriculum. Particular emphasis is placed on Python since data scientists find it the most versatile language with a wealth of libraries explicitly designed for data analysis and manipulation.

** **

These courses also monitor industry expectations so you can be sure you’re ready to take on problems in the real world. Because of how highly portable the skills and knowledge you acquire are, you can investigate prospects in various industries, including e-commerce, healthcare, and finance.

These programmes also emphasise practical experience through interactive projects that mimic real-world situations. Using a project-based approach, you may develop a portfolio that highlights your capabilities and gives prospective employers concrete proof of your ability.

Numerous organisations that offer short-term courses in data science also provide career services and assistance in finding employment. These services can improve your chances of landing a data science position after graduation by helping you with resume development, interview preparation, and networking.

Whether you’re just starting or looking to change careers, a Short Term Job Oriented Data Science with Python Course will prepare you for success and fulfillment in the ever-evolving field of data science by giving you industry-specific knowledge, in-demand skills, and hands-on experience.

For “Freshers” and “Job Seekers,” 3RI provides a program specifically created with a job orientation. We train candidates using working employees with 8–10 years of relevant experience who provide the greatest knowledge and work environments in most IT firms. The training would be supplemented by live projects and placement assistance, preparing the candidates for employment in the sector. The wisest move is to sign up for a machine learning training course at 3RI Technologies. All of the team at 3RI Technologies has solid experience, and they will impart all the abilities that are important for this course.

What’s more, a lot of well-known companies visit 3RI Technologies. Thus, following training, we provide placement. So without a second thought, sign up for our courses that will brighten your career.

**Machine learning training **has become so popular that everyone desires to enroll. The** machine learning certification course** will educate you on many in-demand abilities. List the essential abilities you will acquire during this course.

**Mathematics Skills**

Training in Machine Learning relies heavily on a solid grasp of mathematics. That’s why it’s number one on our list, and one of the earliest skills children acquire in school. Do you find yourself questioning the necessity of math education? (Even more so if you don’t like it?) Indeed, mathematics has many potential applications in ML. Various mathematical formulas can be used to figure out which machine-learning algorithm is ideal for your data. Parameters and confidence levels can also be estimated mathematically. If you’re excellent with numbers, you should have no trouble grasping many ML algorithms because they’re based on standard statistical modeling techniques. You should be familiar with concepts from linear algebra, probability, statistics, multivariate calculus, and distribution theory, including the Poisson, normal, binomial, and other distributions.

**Basics of Computer Science and Programming**

To become an effective machine learning engineer, this is another requirement. You will learn fundamental concepts in computer science, including data structures (such as stacks, queues, trees, and graphs), algorithms (such as searches and greedy programs), space and time complexity, etc. If you already have a bachelor’s degree in computer science, then you know all of this! You would be well-versed in SQL for database management, Apache Kafka for data pre-processing, Spark and Hadoop for distributed computing, and Python and R for machine learning and statistics. Python has become a popular choice for developers, particularly in data science and machine learning. You’ll master the ins and outs of its various libraries during this course, including NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and many more.

**Machine Learning Algorithms**

The teaching of ML algorithms is part of machine learning training. ML algorithms are often grouped into reinforcement, unsupervised, and supervised. The Nave Bayes Classifier, K Means Clustering, Linear Regression, Logistic Regression, Decision Trees, and Random Forests, among others, are the most popular ones. It is advised to acquire a firm grasp of these algorithms before starting a career as an ML engineer.

A specialised educational programme called a Short Term Job Oriented Data Science with Python Course is made to give people the fundamental knowledge and abilities needed to pursue a career in data science, with an emphasis on utilising Python as the primary programming language. This kind is usually distinguished by its briefness; it provides students with a streamlined curriculum that effectively conveys real-world and career-relevant skills in a comparatively short time, usually a few weeks to a few months.

Essential elements of a course like this usually consist of:

** **

**Essential Ideas in Data Science: **This course covers the fundamental ideas in data science, such as statistical modelling, machine learning, data analysis, and data visualisation. These ideas form the basis for more complex subjects.

Python programming: Because of its adaptability and the wealth of data science libraries available, Python is the primary language used in data science (e.g., NumPy, Pandas, and Scikit-learn). The course teaches students how to write code, work with data, and create data science applications by starting from scratch with Python.

** **

Students are taught how to collect and clean data from various sources to prepare it for analysis. This covers managing outliers, addressing missing data, and guaranteeing data quality.

**Exploratory Data Analysis:** An essential component of data science is the exploration and comprehension of data using statistical and visual techniques. Students learn to visualise data, find patterns, and make inferences using Python.

** **

**Machine Learning: **Students will learn how to create prediction models and make data-driven decisions by studying machine learning algorithms in this course. Regression, classification, clustering, and model evaluation are possible subjects.

**Real-world Projects:** Working on practical projects that mimic real-world situations is a great way to get practical experience. Students can use their knowledge to solve data-related challenges through these projects.

** **

**Job Placement Support:** Many short-term, career-focused data science courses provide career services to assist students in finding work. This could involve creating a CV, getting ready for an interview, and helping to find employment.

These courses have a strong emphasis on teaching practical, industry-relevant skills that prepare graduates for careers in data science, which is indicative of their job orientation. Data analysts, machine learning engineers, business intelligence analysts, and other positions may be among them.

A short-term job-oriented data science course using Python offers a focused and expedited education in data science, giving students the tools they need to launch a successful career in this quickly expanding area. It’s an excellent choice for people who want to get into data science jobs fast or who want to expand their skill set to stay competitive in the job market.

Skills Required

- No Prerequisites
- Basic Knowledge of programming is advantageous

### Data Science with Python and Machine Learning Certification Course Syllabus

Best in Class Content by Leading Faculty & Industry Leaders in the form of Videos, Case Studies and Projects, Assignments and Live Sessions.

- Fundamentals of Data Science and Mathematical Statistics

- Introduction to Data Science
- Need of Data Science
- BigData and Data Science
- Data Science and machine learning
- Data Science Life Cycle
- Data Science Platform
- Data Science Use Cases
- Skill Required for Data Science

- Mathematics For Data Science

- Linear Algebra

- Vectors

- Optimization

- Theory Of optimization
- Gradients Descent

- Introduction to Statistics

- Descriptive vs Inferential Statistics
- Types of data
- Measures of central tendency and dispersion
- Hypothesis & inferences
- Hypothesis Testing
- Confidence Interval
- Central Limit Theorem

- Probability and Probability Distributions

- Probability Theory
- Conditional Probability
- Data Distribution
- Distribution Functions

- Normal Distribution
- Binomial Distribution

**An Introduction to Python**

- Why Python , its Unique Feature and where to use it?
- Python environment Setup/shell
- Installing Anaconda
- Understanding the Jupyter notebook
- Python Identifiers, Keywords
- Discussion about installed module s and packages

**Conditional Statement ,Loops and File Handling**

- Python Data Types and Variable
- Condition and Loops in Python
- Decorators
- Python Modules & Packages
- Python Files and Directories manipulations
- Use various files and directory functions for OS operations

**Python Core Objects and Functions**

- Built in modules (Library Functions)
- Numeric and Math’s Module
- String/List/Dictionaries/Tuple
- Complex Data structures in Python
- Python built in function
- Python user defined functions

**4. Introduction to NumPy**

- Array Operations
- Arrays Functions
- Array Mathematics
- Array Manipulation
- Array I/O
- Importing Files with Numpy

**5. Data Manipulation with Pandas**

- Data Frames
- I/O
- Selection in DFs
- Retrieving in DFs
- Applying Functions
- Reshaping the DFs – Pivot
- Combining DFs
- Merge, Join
- Data Alignment

**6. SciPy**

- Matrices Operations
- Create matrices
- Inverse, Transpose, Trace, Norms , Rank etc
- Matrices Decomposition
- Eigen Values & vectors
- SVDs

**7. Visualization with Seaborn**

- Seaborn Installation
- Introduction to Seaborn
- Basics of Plotting
- Plot Generation
- Visualizing the Distribution of a Dataset
- Selection color palettes

**8. Visualization with Matplotlib**

- Matplotlib Installation
- Matplotlib Basic Plots & it’s Containers
- Matplotlib components and properties
- Pylab & Pyplot
- Scatter plots
- 2D Plots
- Histograms
- Bar Graphs
- Pie Charts
- Box Plots
- Customization
- Store Plots

**9. SciKit Learn**

- Basics
- Data Loading
- Train/Test Data generation
- Preprocessing
- Generate Model
- Evaluate Models

**10. Descriptive Statistics**

- Observations, variables, and data matrices
- Types of variables
- Measures of Central Tendency
- Arithmetic Mean / Average
- Merits & Demerits of Arithmetic Mean and Mode
- Merits & Demerits of Mode and Median
- Merits & Demerits of Median Variance

**11. Probability Basics**

- Notation and Terminology
- Unions and Intersections
- Conditional Probability and Independence

**12. Probability Distributions**

- Random Variable
- Probability Distributions
- Probability Mass Function
- Parameters vs. Statistics
- Binomial Distribution
- Poisson Distribution
- Normal Distribution
- Standard Normal Distribution
- Central Limit Theorem
- Cumulative Distribution function

**13. Tests of Hypothesis **

- Large Sample Test
- Small Sample Test
- One Sample: Testing Population Mean
- Hypothesis in One Sample z-test
- Two Sample: Testing Population Mean
- One Sample t-test – Two Sample t-test
- Paired t-test
- Hypothesis in Paired Samples t-test
- Chi-Square test

**14. Data Analysis**

- Case study- Netflix
- Deep analysis on Netflix data

**Exploratory Data Analysis**

- Data Exploration
- Missing Value handling
- Outliers Handling
- Feature Engineering

**Feature Selection**

- Importance of Feature Selection in Machine Learning
- Filter Methods
- Wrapper Methods
- Embedded Methods

**Machine Learning: Supervised Algorithms Classification**

- Introduction to Machine Learning
- Logistic Regression
- Naïve Bays Algorithm
- K-Nearest Neighbor Algorithm
- Decision Tress
- SingleTree
- Random Forest

- Support Vector Machines
- Model Ensemble
- Model Evaluation and performance
- K-Fold Cross Validation
- ROC, AUC etc…

- Hyper parameter tuning
- Regression
- classification

**Machine Learning: Regression**

- Simple Linear Regression
- Multiple Linear Regression
- Decision Tree and Random Forest Regression

**Machine Learning: Unsupervised Learning Algorithms**

- Similarity Measures
- Cluster Analysis and Similarity Measures

**Ensemble algorithms**

- Bagging
- Boosting
- Voting
- Stacking
- K-means Clustering
- Hierarchical Clustering
- Principal Components Analysis
- Association Rules Mining & Market Basket Analysis

**7. Recommendation Systems**

- Collaborative filtering model
- Content-based filtering model.
- Hybrid collaborative system

**Introduction to Data Visualization & Power of Tableau**

- Architecture of Tableau
- Product Components
- Working with Metadata and Data Blending
- Data Connectors
- Data Model
- File Types
- Dimensions & Measures
- Data Source Filters
- Creation of Sets

**2. ****Scatter Plot**

- Gantt Chart
- Funnel Chart
- Waterfall Chart
- Working with Filters
- Organizing Data and Visual Analytics
- Working with Mapping
- Working with Calculations and Expressions
- Working with Parameters
- Charts and Graphs
- Dashboards and Stories

- Machine Learning end to end Project blueprint
- Case study on real data after each model.
- Regression predictive modeling – E-commerce
- Classification predictive modeling – Binary Classification
- Case study on Binary Classification – Bank Marketing
- Case study on Sales Forecasting and market analysis
- Widespread coverage for each Topic
- Various Approaches to Solve Data Science Problem
- Pros and Cons of Various Algorithms and approaches

Free Career Counselling

We are happy to help you 24/7

Who can apply for the course?

- Any graduates who want to start their career in Data Science.
- BE / BTech / MCA passed aspirants to make their career in Machine Learning.
- IT-Professionals who want to get career in Data Science with Python
- Professionals from non-IT bkg, and want to establish in IT.
- Candidate who would like to restart their career after a gap.
- Linux Engineer, System Admins can go for next level of their career.

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Industry Projects

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- Project Implementation with Real-Time Scenario.

Dedicated Industry Experts Mentors

Receive 1:1 career counselling sessions & mock interviews with hiring managers. Further your career with our 300+ hiring partners.

Batch Schedule

Schedule Your Batch at your convenient time.

29-June-24 | SAT-SUN 8:00 AM to 10:00 AM

08-July-24 | MON-FRI 8:00 AM to 10:00 AM

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## Will I Get Certified?

Upon successfully completing this program, you’ll earn a certificate.

The 3RI certification is accepted and respected by every significant multinational company across the nation. Fresh graduates and corporate trainees are eligible for the assistance. We offer certificate once the academic and practical courses have been finished. The certification that we offer here at 3RI is recognized across the country. The value of your resume will grow as a result. With the assistance of this qualification, you will be able to obtain prominent employment posts in the most successful multinational corporations in the country. The completion of our course as well as the projects that are based on practical application, are prerequisites for receiving the certificate.

### data science with python and Machine Learning Certification

##### Frequently Asked Questions

Of course, Before **machine learning training**, it is crucial to master Python and data science because, without these skills, you will find it difficult to implement well-known libraries and write scalable code that other engineers can work on. Today, Python holds a dominant position in the field of machine learning. Python is a good choice for machine learning projects because of its consistency in syntax, ease of use, and quick development time.

Mathematics, data science, computer science, and computer programming are the most important skills for a machine learning engineer; a prospective machine learning engineer should get an undergraduate degree in one of those fields. Those who have all of the skills above can take this course.

Machine Learning Crash Course assumes or requires no prior machine learning experience. However, for students to comprehend the provided topics and finish the activities, the following requirements are recommended:

- Variables, linear equations, function graphs, histograms, and statistical means must familiarize you.
- You must be a competent coder. Because the programming tasks are in Python, you should ideally have programming experience in Python.

Yes, This course is appropriate if you wish to get knowledge of the statistical methods required for Data Science with Python. To become a professional data scientist, it is needed to study statistics. This course is meant for both novices with no prior experience in statistics for data science and those wishing to expand their expertise in the statistics field using Python.

Yes, A rewarding professional path is data science with Python. Between March 2020 and April 2022, the number of data science jobs in India will increase by 73.5%. By 2026, there will be around 11 million unfilled positions in India. According to LinkedIn, data science is the field with the fastest growth rate, and there are many opportunities. After full-stack developers, data scientists have the highest salaries in the IT sector. The current survey states that the wage range for data scientists in India is from 4.5 lakhs to 25.4 lakhs per year, with the average salary being 10.5 lakhs per year.

This Data Science with Python course is meant to teach you how to do Data Science tasks with Python Programming. You will know a lot about statistics and then see how they work in a case study. Then you will know how to change data, how different distributions work, and what a histogram is. You will learn more about Python programming to understand some of Python’s most important libraries, like NumPy, Pandas, Matplotlib, and Seaborn. This course will also teach you about regression models, data analytics, and different ways to display information. You can see how much you’ve learned from this course by taking the quiz and getting a certificate of completion.

Yes, Knowledge of computer languages like Python and R is required to implement the entire machine-learning process. Both Python and R include built-in packages that make using machine learning methods fairly simple.

With a machine learning course, you can obtain a high-paying position as a Machine Learning Engineer, Data Scientist, NLP Scientist, Business Intelligence Developer, or Human-Centered Machine Learning Designer.

Depending on your degree or certification, there are several machine learning courses. You can learn machine learning skills through 4-month courses at 3RI Technologies to be eligible for entry-level jobs at prestigious companies.

The Data Science with Python course teaches essential Python programming concepts, such as data transfer, directory processes, object-oriented programming, and several Python frameworks required for data science. This course would be beneficial for both novices and professionals. This course’s introduction to machine learning models, reinforcement learning, and other essential concepts will set them up for success in data science.

Data science is not only the latest trend; it is also the way of the future. When planning your career, you should consider the present and the future. Python has gotten a lot of attention, which is good. Python is a powerful and easy-to-use programming language. Here are some good things that would happen if you took Python training to learn data science.

**Easy To Learn –**Python is a language that is one of the easiest to learn. Even if you have never coded before, learning Python won’t be hard. When people hear about becoming a data scientist, one of the main things that stop them is that they don’t know how to code and think it will be hard to learn. With Python, you won’t have to worry about this.**Strong Packages**– Python also has various packages, like NumPy, SciPy, PyBrain, Pandas, and others, making it exceptionally easy to write difficult data analytics tasks. Additionally, many libraries enable the connection of Python to other programming languages like C and SQL. These help Python become even more potent thanks to these.

In short, the answer to the question above is a big NO! Data Science is hard to learn, which is mostly a mistake people make when they are just starting. As they learn more about data science, they realize that it is just another field of study that can be learned by working hard.

Data Scientists use Python the most, their favorite programming language. One of the main reasons why Python is so popular in the field of Data Science is that it is easy to use and has a simple syntax. This makes it easy for people who don’t have a background in engineering to learn and use. Also, there are a lot of open-source libraries and online guides for putting Data Science tasks like Machine Learning, Deep Learning, Data Visualization, etc., into action. So, it’s not hard to learn Data Science with Python.

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