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Data Science with Python Certification Training Course With Placement Assurance
Become an IABAC Certified Data scientist in 6 weeks, with 100% money back guaranteed if you are not happy with the training.
2000+ Ratings
3000+ Happy Learners
Data Science with Python Videos
Skills Covered in Data Science
Python full coding from scratch
Visualization with Python
Machine Learning with Python - 6 different algorithms
Robotic Automation
Multiple Linear regression
Logistic regression
Cluster Analysis
SQL queries(with Python)
Machine Learning Fundamentals
Data Manipulation with Pandas
Statistical Analysis with Python
Supervised and Unsupervised Learning
Building Predictive Models
Time Series Analysis
Model Evaluation and Optimization
Data Science Training Key Features
63 Hrs Practical Sessions
Placement assistance will be provided
Assured Job Placement
Professional Resume building
Interview Preparation Session
Classroom & Online Training
Zero-Interest EMI Option
Delivered by Industry Experts
18 Case studies on Machine Learning
Certification Guidance
1 Year Access to Recorded Sessions
Zero-Interest EMI Option
Weekly Practice Assignments
Certification Guidance
Designated Placement Advisor
Weekly Practice Assignments
Placement assistance will be provided
Cloud Computing will be offered as a complimentary course
Our Alumni Working in
Data Science Course Reviews
Resham Rajpal
Data Science Expert
The training at Apponix made me more confident in Data analysis. I have completed Data Science training form here. Excellent classes that helped a lot.
Ajith Bahadhur
Data Science Analyst
My experience with Apponix was really great. The trainer provided a lot of practical training. He is an expert. Each topic was taught through proper practical classes.
Mahesh Mahi
Data Science Analyst
I took Data Science training from the institute. I am happy to make Apponix as my choice. Too good labs and sessions are handled here.
Lakshana Senthil
Data Engineer
Management is very good, flexible with timings. Teaching pattern is easy and understandable.
Bijal Patel
Data Analyst
Best institute for Data Science. Trainer has very good knowledge in Data Science.
Aarsh Mehta
Data Engineer
Sir is very helpful and takes every step to make the students understand topics.
Our Recent Placements
Classroom Training
Classroom Training
Fees & Training Options
Online Training
- Interactive Live Training Sessions
- 60+ Hrs Practical Learning
- Delivered by Working Professionals
- 1 Year Access to Recorded Sessions
- Placement assistance will be provided
- Daily 2 Hrs or Weekend 10 Hrs
- Weekdays & Weekend batches
- Authorized IABAC Training Delivery
- Doubt Clearing Sessions
- Regular assignments will be given and assessments will be taken.
Data Science Training Syllabus
Prerequisites
No prerequisites for learning data science but if you have some previous knowldge on computer science & statistics it helps you a lot in understanding subjects fast.
Secondly, you need to have an analytical mind – that is all you would need to enroll for Statistics For Data Science Courses.
Data Science Course Syllabus
Overview of the course
Class 1: Introduction to Python Programming Language
- Introduction and Installation of Python software Python packages: Pandas, & Numpy
- Concepts of Data frame Filtering
- Loc and iloc for filtering Usage of Boolean in Filtering Appending
Class 2: Data handling in Python
- Handling of Missing values If else statement
- Extra trick of using if else statement Removal of Duplicates
- Frequency Distribution
- Merging – Inner, Outer, Left and Right Binding and Appending
- Descriptive Statistics o Inbuilt Numeric functions of R
Class 3: More data handling using Python
- Pivot Table of Excel in Python Grouping function
- Learning of SQL queries using Python Grouping numeric data
Class 4: Additional functions of Python
- Text functions
- Data cleaning with efficient text functions Inbuilt String functions of Python Reshape functions of Python
Class 5: Statistics
- Everything you want to know about statistics….Well sort of!! Mean, Median, Mode
- Standard Deviation, Variance, Normal Distribution Hypothesis testing
- T-test, Anova, Normality test
Class 6: Linear Regression
Class 7 : Linear Regression Practice Case Study
- Predictive Analytics – Linear Regression Concepts of Linear Regression
- Simple and Multiple Linear Regression Automatic Dummy Variablescreation technique Model Validation parameters
- Model Assumption testing
- Splitting of data for Validation and testing
- Business Case Study with real data to model in Python
Participants will be asked to develop a Linear Regression model on a real life data, in presence of the instructor. Time given is 2.5 hours. Participants will be treated like an industry employee, but in terms of help certainly the instructor will not be as ruthless as the boss. After completion of the model (with the help of the instructor wherever it is required), the instructor will show how to present a model to a real life client.
Class 8: Logistic Regression
Class 9: Logistic Regression Practice Case Study
- Predictive Analytics – Logistic Regression Concepts of LogisticRegression
- Difference between Linear Regression and Logistic RegressionAutomatic Dummy Variables creation technique
- Model Validation parameters Model Assumption testing
- Splitting of data for Validation and testing
- Business Case Study with real data to model in Python
Participants will be asked to develop a Logistic Regression model on a real life data, in presence of the instructor. Time given is 2.5 hours. Participants will be treated like an industry employee, but in terms of help certainly the instructor will not be as ruthless as the boss. After completion of the model (with the help of the instructor wherever it is required), the instructor will show how to present a model to a real life client.
Class 10: Time Series Forecasting
- Time series forecasting: ARIMA
- Difference between forecasting and prediction Concepts of time series data
- Concepts of ARIMA
- Descriptive analytics for ARIMA Development of model
- Best model selection Forecasting with the best model Residual analysis
- Business Case Study with real data to model in R software
- Participants will be asked to develop a model in presence of the instructor.
Class 11: Cluster Analysis
- Unsupervised Machine Learning with Python Cluster Analysis: Concepts
- Cluster analysis with Python – K Means, Hierarchical etc.
Class 12: Decision Tree and Random Forest
- Concepts of Decision Tree Decision Tree with Python Concepts ofRandom Forest Random Forest with Python
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Our Top Instructors
Mr Shubhojit
Work Experience:
Shubhojit brings over 14 years of experience in Analytics, having worked with top CMMI Level 5 companies such as The Nielsen Company and TCS, where he currently serves as a Senior Analytics Consultant.
He has domain expertise across Retail, BFSI, Energy, and Utilities, and specializes in Predictive Analytics, Machine Learning, and Simulation using tools like SAS, R, Python, and GMP.
Proficient in advanced algorithms including ANN, RNN, CNN, XGBoost, Naïve Bayes, and SVM. He has built classification, regression, and churn models using Python and R.
Experienced in NLP for sentiment analysis, topic modeling, and text analytics. He has also applied recommendation systems such as Collaborative Filtering and Market Basket Analysis.
Shubhojit has utilized tools like Power BI, Spotfire, and REST APIs for building insightful dashboards and data integration, and used Google Analytics and HotJar for web and customer behavior analytics.
Trained 250+ students and conducted corporate sessions for Genpact, HSBC, ITC, and Wipro; also invited to IIT Kharagpur for workshops.
He had also been invited to IIT Kharagpur several times, to conduct analytics workshops for their B-Tech students. He has founded Analytics Educator – a premium institute for pertaining analytics training to professionals.
Founder of Analytics Educator and associated with institutions like Apponix, IVY Pro School, and EduPristine for professional analytics training.
Santosh
Education:
Graduate of Oxford College, where I developed a strong foundation in data science and analytics.
Professional Experience:
With over 10 years of experience in the industry, I am currently a Senior Data Science Specialist at Microsoft, specializing in advanced data analytics solutions for leading MNCs.
Technical Skills & Key Technologies:
- Predictive Modeling: Building models to forecast outcomes using historical data.
- Natural Language Processing (NLP): Analyzing and interpreting text data for insights.
- Machine Learning with Python: Skilled in applying multiple ML algorithms to solve real-world problems.
- Data Visualization: Creating impactful visuals using Python for better data storytelling.
- Robotic Process Automation (RPA): Streamlining tasks and boosting efficiency through automation.
Student Success & Career Support:
- Trained over 1000 students at Apponix Academy with an average rating of 4.9. I provide:
- Career Mentorship: Resume building, mock interviews, and personalized job readiness coaching.
- Placement Support: Assisting students in securing roles at top organizations through industry-focused guidance.
Subojith
Education:
Graduate from CMR University, where I built a strong foundation in data science principles and practices.Professional Experience:
With 15+ years of experience in the data science domain, I currently serve as a Senior Data Science Manager at Capgemini, bringing deep insights and knowledge from various MNCs.Technical Skills:
- Logistic Regression: Skilled in statistical modeling for binary outcomes and predictive analysis.
- SQL Queries with Python: Proficient in data manipulation and analysis using integrated SQL and Python workflows.
- Time Series Analysis: Experienced in identifying trends and patterns in temporal data.
- Cloud Computing (AWS, Azure, GCP): Leveraging cloud platforms for scalable data storage and analytics.
Student Success & Career Support:
- Trained over 1500 students at Apponix Academy with an average rating of 4.9, offering strong career guidance through:
- Resume Building: Crafting job-ready, data science-focused resumes.
- Interview Prep: Conducting mock sessions and sharing strategic tips.
- Placement Support: Assisting students in landing roles across top companies.
Keerthan
Education:
Graduated from Reva University, where I laid the foundation for my expertise in data science.
Professional Experience:
With 9+ years of experience in the industry, I currently serve as a Senior Data Science Specialist at Wipro, bringing valuable insights from my work with various MNCs.
Technical Skills:
- Machine Learning with Python: Proficient in applying 6 different algorithms to solve real-world problems.
- Robotic Automation: Expertise in automating processes to enhance efficiency.
- Multiple Linear Regression: Skilled in statistical modeling to understand relationships between variables.
- Data Visualization: Utilizing tools like Tableau and Matplotlib to present insights effectively.
- Big Data Technologies: Experience with frameworks like Hadoop and Spark for large-scale data processing.
Student Success & Career Support:
- I have trained over 900 students at Apponix Academy, achieving an average rating of 4.8. My commitment to student success includes:
- Resume Building: Helping students craft impactful resumes tailored for data science roles.
- Interview Preparation: Providing practical guidance and mock interviews to enhance confidence and skills.
- Job Placement Assistance: Supporting students in navigating the job market to secure fulfilling positions.
Arpitha
Education:
Alumnus of St. Joseph’s College, where I honed my analytical skills and passion for data science.
Professional Experience:
With 12+ years of experience, I currently serve as a Senior Data Science Specialist at Amazon, where I apply my expertise to solve complex data challenges in a fast-paced environment.Technical Skills & Expertise:
- Data Engineering & Wrangling: Skilled in ETL processes, cleaning, and preparing data for analysis.
- Experimental Design & A/B Testing: Designing experiments to support data-driven decisions.
- Model Deployment & Time Series Analysis: Deploying models and analyzing sequential data trends.
- Computer Vision & Web Scraping: Extracting insights from images, videos, and online data sources.
- Big Data & Visualization: Proficient with Hadoop, Spark, and creating impactful data visualizations.
Student Success & Career Support:
- Trained over 1200 students at Apponix Academy with an average rating of 4.8. I offer end-to-end support through:
- Resume Building: Creating impactful, job-ready resumes.
- Interview Prep & Placement: Mock interviews and personalized guidance to secure data science roles.
Overview of Data Science Training Course
- Our Data Science with Python certification training provides students with a comprehensive understanding of data science and how to use Python for data analysis. The training covers fundamental concepts, tools, and techniques used in data science, along with hands-on experience working with Python libraries.
- Our experienced trainers provide personalized instruction to help students develop proficiency in key areas such as data manipulation, data visualization, and machine learning. Through the training, students will gain practical experience in real-world data science scenarios, preparing them for success in the field.
- The Data Science with Python certification is designed for professionals looking to upskill or transition into a career in data science. With the increasing demand for data-driven decision-making, the ability to use Python for data analysis is becoming an essential skill for professionals in a variety of industries.
- Our Data Science with Python certification training is designed to equip students with the skills and knowledge needed to succeed in the rapidly growing field of data science, and our experienced trainers are dedicated to ensuring that each student achieves their goals.
Benefits of learning Data Science
- Our Data Science with Python certification training provides students with a comprehensive understanding of data science and how to use Python for data analysis. The training covers fundamental concepts, tools, and techniques used in data science, along with hands-on experience working with Python libraries.
- Our experienced trainers provide personalized instruction to help students develop proficiency in key areas such as data manipulation, data visualization, and machine learning. Through the training, students will gain practical experience in real-world data science scenarios, preparing them for success in the field.
- The Data Science with Python certification is designed for professionals looking to upskill or transition into a career in data science. With the increasing demand for data-driven decision-making, the ability to use Python for data analysis is becoming an essential skill for professionals in a variety of industries.
- Our Data Science with Python certification training is designed to equip students with the skills and knowledge needed to succeed in the rapidly growing field of data science, and our experienced trainers are dedicated to ensuring that each student achieves their goals.
Data Science related jobs
- Data Analyst
- Data Scientist
- Data Engineer
- Data Architect
- Analytics Manager/Lead
- Machine Learning Engineer
- Statistical Programming Specialist
Data Science Courses
What is the scope of data science career?
Because it is rated as one of the top job sectors in terms of career growth opportunities, job satisfaction and pay scale. Furthermore, the demand for data scientists is always high as the number of skilled professionals is comparatively less.
Can I learn data science even if I don’t have any prior knowledge about this field?
Yes, you can learn data science even if you do not have any prior knowledge about this field as we will also take classes on basic concepts of data science.
Our complete data science Bootcamp does not come with exams and certification but an industry-recognized data science course completion certificate will be offered after you score 80 % at the one-hour-long exam we will conduct that will contain 25 objective-type questions about the course.
Data science is an umbrella term for the processes a data scientist uses to uncover patterns all the while extracting business-critical information from huge amounts of data derived from various sources.
Why Should You Learn Data Science ?
- 28-35% of boost in the field since 2025
- Expecting massive recruitment
- Handsome salary ranges from around 3.5 to 10 lakhs per year
- Majority of the firms are moving to be insights-drive which increases the trends in and around big data, AI, cloud computing, etc. This enhancement of data maturity heightened the need of data science
- Huge investment of MNCs in Big Data and AI sectors which demands greater implementations of Data Science
- Along with analyzing and comprehending the data trends, a data scientist has to interpret and decipher the laws in an appropriate and approachable manner for the business tycoons, and also formulate advance solutions over it.
Python and Machine Learning
Python's versatility and extensive library support have made it the go-to language for machine learning and artificial intelligence (AI) applications.
Machine learning, a subset of AI, revolves around training algorithms to make predictions or decisions based on data. Python's strengths align seamlessly with the requirements of this field.
Ecosystem: Python boasts a wealth of libraries and frameworks dedicated to machine learning, such as TensorFlow,PyTorch, Scikit-Learn, and Keras. These libraries provide pre-built tools and models that streamline the development process.
Ease of Learning: Python's simplicity and readability lower the barrier to entry for newcomers to machine learning. Its syntax resembles pseudocode, making it accessible for those
with varying levels of programming experience.
Data Manipulation: Libraries like NumPy and pandas enable efficient data manipulation, transformation, and analysis. These tools are essential for preprocessing datasets before training machine learning models.
Visualization: Python's libraries like Matplotlib and Seaborn allow for easy data visualization, helping data scientists and machine learning engineers explore datasets and present results effectively.
Community and Support: Python has a thriving community of data scientists, machine learning practitioners, and researchers. This community provides extensive documentation,
tutorials, and forums for sharing knowledge and troubleshooting issues.
Scalability: Python is suitable for both prototyping and scaling machine learning projects. Developers can initially build models quickly and then optimize
them for production use.
Integration: Python can be seamlessly integrated into various data science and machine learning workflows. It can work with databases, web APIs, and other technologies commonly used in data-driven applications.
Deployment: Python offers various deployment options for machine learning models, including web services, containerization (e.g., Docker), and cloud platforms (e.g., AWS, Azure, Google Cloud).
Community Packages: Beyond machine learning, Python's ecosystem includes packages for natural language processing, computer vision, reinforcement learning, and more,
broadening its applications within AI.
Data Science Course Objectives
- Data science is a rapidly growing field, and proficiency in Python is becoming an essential skill for professionals in a variety of industries. Our Data Science with Python Certification Training Course provides hands-on experience working with Python libraries, giving you practical skills that you can apply in real-world data science scenarios. Here are some reasons why you should consider taking our course:
- Career Advancement: With the increasing demand for data science professionals, proficiency in Python can help you advance your career or transition into a new role. Our certification can demonstrate your expertise in the field, giving you a competitive edge in the job market and enhancing your professional credibility.
- Comprehensive Understanding: Our course covers fundamental concepts, tools, and techniques used in data science, providing you with a comprehensive understanding of the field. You will learn how to use Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn to manipulate and analyze data, build predictive models, and visualize data.
- Hands-On Experience: Our course provides hands-on experience working with real-world datasets, giving you practical skills that you can apply in your job. You will work on projects throughout the course, giving you the opportunity to practice what you've learned and build a portfolio of data science projects.
- Personalized Instruction: Our experienced trainers provide personalized instruction to help you develop proficiency in key areas, such as data manipulation, data visualization, and machine learning. You will receive feedback and guidance throughout the course, helping you to improve your skills and achieve your learning objectives.
- Flexibility: Our course is flexible and can be taken online, allowing you to learn at your own pace and on your own schedule. You can access the course materials and assignments from anywhere, and our trainers are available to answer your questions and provide support throughout the course.
Why is Data Science with Python more popular?
- Python is one of the most popular programming languages used in data science, and for good reason. Python is easy to learn and use, has a large community of developers, and offers a wide range of libraries and tools for data analysis and machine learning. Python is also a versatile language that can be used for a variety of applications, from web development to scientific computing.
- Data Science with Python is becoming more popular because it offers a powerful combination of data analysis tools and programming capabilities. With Python, you can quickly and easily manipulate large datasets, build predictive models, and visualize data. Python also offers a wide range of libraries and frameworks for machine learning, including Scikit-learn, TensorFlow, and PyTorch.
Job Opportunities for Data Science with Python professionals in 2025
- The demand for data science professionals with expertise in Python is expected to continue to grow in the coming years. According to a report by LinkedIn, data science is one of the fastest-growing job sectors, with a projected growth rate of 37% by 2025. In addition, Python is one of the most in-demand programming languages, with a 27% increase in job postings between 2018 and 2021.
- Data science professionals with expertise in Python are highly sought after by a variety of industries, including healthcare, finance, retail, and technology. Some of the job titles that you may be qualified for after completing our Data Science with Python certification course include:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
- Data Engineer
- With the skills and knowledge gained from our certification course, you can position yourself for a rewarding career in the rapidly growing field of data science.
