Data Science With Python Certification Training
Key Giveaways Of Best Data Science Training
60 hours of blended data science learning
4 industry-based projects
Case Studies based on the latest scenario
Interactive learning with practical sessions
Lifetime access to self-paced learning
Dedicated mentoring session from faculty of industry experts
24x7 support
Authorized material
100% support from the authorized trainer
Steps To Get Certified
Pre-requisites Of Databases & SQL For Data Science With Python
- Anyone with 0-3 years of work experience
- College students in the last year of their graduation or post-graduation
- Anyone looking for a career transition to data science and machine learning
- IT professionals
- Technical and Non Technical domain professionals/freshers can also apply
- Freshers who want to pursue a career in data science and machine learning
How To Learn Python For Best Data Science Visualization Tools
Step 1: Learn Python Fundamentals
Step 2: Practice Mini Python Projects
Step 3: Learn Python Data Science Libraries
Step 4: Build a Data Science with Machine Learning Course Portfolio as you Learn Python
Course Curriculum
Live Instructor Led Best Data Science Training
Data Science with Python for Career Growth
Course Objectives Of Best Data Science Course In India
- Gain an in-depth understanding of data science methods, data wrangling, data exploration, data visualization, hypothesis building, and testing. You’ll additionally learn the fundamentals of statistics.
- Install the specified Python environment and other auxiliary tools and libraries
- Understand the essential ideas of Python programming like data types, tuples, lists, dicts, basic operators, and functions.
- Perform high-level mathematical computing using the NumPy package and its massive library of mathematical functions
- Perform scientific and technical computing using the SciPy package and its sub-packages like Integrate, Optimize, Statistics, IO, and Weave.
- Gain experience in machine learning using the Scikit-Learn package
- Extract useful data from websites by performing web scrapping using Python
What roles can a Data Science professional play?
Machine Learning Expert
With the help of several machine learning tools and technologies, they build statistical models with large chunks of business data.
Senior Data Scientist
They understand the issues and create models based on the data gathered and manage a team of data scientists.
Applied Scientist
They design and build machine learning models to derive intelligence for the services and products offered by the organization.
AI Expert
They build strategies on frameworks and technologies to develop AI solutions and help the organization prosper.
Big Data Specialist
They create and manage pluggable service-based frameworks that are customized to import, cleanse, transform, and validate data.
Not sure how to get started? Let our Learning Advisor help you.
Testimonial
FAQs
In terms of business and job opportunities, data science is one of the emerging fields. Python is a popular programming language that has evolved into the language of choice for data scientists. Learning Python with Data Science increases your chances of being hired as a skilled data scientist.
The Data Science with Python course has been thoughtfully designed to prepare you to take on significant roles in top tech companies as a dependable Data Scientist. You will be able to do the following at the end of the course:
- Create Python programmers: distribution, user-defined functions, dataset import, and more.
- Using the Pandas library, you can manipulate and analyses data.
- Python libraries for data visualization include Matplotlib, Seaborn, and pilot.
- Data distribution: variance, standard deviation, and interquartile range
- Hypothesis testing is used to calculate conditional probability.
- Variance Analysis (ANOVA)
- Creating linear regression models, assessing model parameters, and calculating performance metrics
- Making Use of Dimensionality Reduction Techniques
- Creating Binomial Logistic Regression models, assessing model parameters, and calculating performance metrics
- Creating KNN algorithm models to determine the best value of K
- Developing Decision Tree models for both regression and classification issues
- Create Python programmed: distribution, user-defined functions, dataset import, and more.
- Using the Pandas library, you can manipulate and analyses data.
- Data visualization libraries for Python include Matplotlib, Seaborn, and pilot.
- Create data distribution models such as variance, standard deviation, and interquartile range.
- Hypothesis Testing is used to compute conditional probability.
- Conduct a variance analysis (ANOVA)
- Create linear regression models, test model parameters, and track performance metrics.
- Make use of Dimensionality Reduction.
- Create Logistic Regression models, test model parameters, and track performance metrics.
- Apply K-means Hierarchical Clustering and Clustering
- Create KNN algorithm models to determine the best value of K.
- Create Decision Tree models that can be used for both regression and classification problems.
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Our programmers is tailored to people with varying levels of Data Science expertise. The data science with Python course covers everything you need to know, whether you're a beginner or an expert, from the fundamentals to advanced concepts in Data Science.
Yes, our applied Data Science with Python course is designed to give you the freedom to advance at your own pace. We have weekday and weekend batches to accommodate your current work schedule.
There are no prerequisites for this practical Data Science with Python certification course, but prior knowledge of basic programming, preferably in Python, would be beneficial.
The technical skills required to become a data scientist are listed below.
- Mathematics - While a Ph.D. is not required, a basic understanding of linear algebra, algorithms, and statistics is required.
- Machine Learning - Set yourself apart from the crowd by learning ML techniques such as logistic regression, decision trees, supervised machine learning, and so on. These abilities will aid in the resolution of various data science problems.
- Coding - A data scientist must be able to manipulate codes in order to analyses data. Python is a popular and simple programming language.
You will have the opportunity to work on a capstone project by the end of the course. The project is based on real-life scenarios and is being carried out with the help of industry experts. You will approach it in the same way that you would approach a data science project in the real world.
The following is a road map to becoming a data scientist:
- Getting Started: Select a programming language with which you are familiar. Python is a programming language that we recommend.
- Mathematics and Statistics: The science in Data Science is all about dealing with data (whether numerical, textual, or visual), identifying patterns, and establishing relationships between them. You must be familiar with basic algebra and statistics.
- Data Visualization: Data visualization is one of the most important steps in this learning path. You must simplify it as much as possible so that the other non-technical teams can understand its contents as well. It is critical to learn data visualization in order to communicate more effectively with end users.
- ML and Deep Learning: Having deep learning skills on your CV, in addition to basic ML skills, is a must for any data scientist, as it is through deep learning and ML techniques that you will be able to analyses the data given to you.
This course will enable you to:
- Gain an in-depth understanding of data science methods, data wrangling, data exploration, data visualization, hypothesis building, and testing. You’ll additionally learn the fundamentals of statistics.
- Install the specified Python environment and other auxiliary tools and libraries
- Understand the essential ideas of Python programming like data types, tuples, lists, dicts, basic operators, and functions.
- Perform high-level mathematical computing using NumPy package and its massive library of mathematical functions
- Perform scientific and technical computing using SciPy package and its sub-packages like Integrate, Optimize, Statistics, IO, and Weave.
- Gain experience in machine learning using the Scikit-Learn package
- Gain an in-depth understanding of supervised learning and unsupervised learning models for example linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
- Use Scikit-Learn package for natural language processing
With Tromenz, you can receive your training via its own Learning Management System. Live and interactive instructor-led training is also available in batches. You will have the opportunity to listen, learn, ask questions, and get all of your doubts cleared up by your instructor, who is an experienced practitioner.
The trainers of our Data Science Python courses are experienced professionals and experts with a wealth of experience leading, applying, coaching, and teaching Data Science Python Courses. Our team of coaches is dedicated to providing Data Science Python training that meets the high standards set by PMI. No matter who the trainer is or where they are located, all candidates receive the same high-quality content. During your time with them, you will be able to further develop your skills and talents to impact your organization.
You will receive the Data Science Python course schedule, workbooks, and all relevant assignments, assessments, or case studies as part of the course materials.
It is possible to access the course using a phone or tablet as our Learning Management System allows it smoothly; however, it is encouraged to have a desktop computer for a better viewing experience.
If you cannot attend a class, you may access the recorded version of the course through the LMS. Every session will begin with a recapitulation of the previous session lasting approximately 10-12 minutes.
Please feel free to contact us at info@tromenzlearning.com if you have any further questions.
Scholarships are available to students and veterans with grants ranging from 10% to 30% of the total course fee.
To apply for the scholarships, please get in touch with us at info@tromenzlearning.com. Forms and instructions will be provided to you by the team. Our panel of experts makes the final decision based on our responses and answers. It is important to note that the entire process could take between seven and fifteen days.
We offer group discounts for groups as small as three (3) participants—the greater the number of participants attending a training course, the greater the value. In most cases, you will save up to 30% by registering in groups. See the upcoming schedule for more info or mail to info@tromenzlearning.com.
If you wish to take advantage of the installment option, please get in touch with us at support@tromenzlearning.com. You will explain how the installments work and provide an estimated timeline. Most courses require two to three installments, but the total amount must be paid before completing the course