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Data Science with Python Certification Training in Los Angeles 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 Assurance
- Guaranteed 10 Interview Arrangements
- Weekdays & Weekend batches
- Authorized IABAC Training Delivery
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
Call Us On
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Our Top Instructors
Mr Shubhojit
WORK EXPERIENCE:-
He has 14 years of experience in the field of Analytics and has worked for different CMMI Level 5 companies like The Nielsen Company (Advanced Analytic Consulting- Price and Promotion team), and TCS. Presently employed with the largest IT company in India - TCS.
Shubhojit has experience in working in multiple domains like Retail, BFSI, Energy, Utility, etc.
An expert in multiple statistical software like SAS, R, Python, Spotfire, GMP, etc. He is a seasoned professional with extensive experience in Predictive Analytics, Machine Learning, and Simulation.
Extremely proficient in handling Deep learning algorithms like Artificial Neural Network (ANN), recurrent neural network (RNN), and convolutional neural network (CNN).
Performed both Regression and Classification models using Deep learning technologies.
This algorithm was executed with both Python and R. He has also worked with other algorithms like XGBoost, Naïve Bayes, Support Vector Machine, or recommendation engines like Collaborative Filtering, and Market basket analysis.
Worked with unstructured data using Natural Language processing to deploy sentiment analysis and topic modeling.
Extensive work experience in visualization tools like Power BI, and Spotfire. The tools were used to connect with different data sources and integrate them to create visualizations for the client to understand their business position.
He had also used to call Rest API to connect to databases where security concerns were of the highest importance and direct data integration was denied.
He has also worked for web analytics using Google Analytics to track visitors’ behavior and predict their patterns. This was also integrated with HotJar to create a heat map of customer clicks.
He has undertaken specific modeling projects for clients including translating client requirements into project specifications, model execution, resolving data, technical and analytical issues, interpreting output, and preparing client presentations and simulators, using Python, R, SAS 9.2, MS Excel, and other software.
Provided required explanation internally to ensure effective use of output, liaising with AAC Consultants as appropriate. Translated model results into client deliverables and present back to the internal team. Communicated with internal team and client for obtaining information.
Handled projects for companies like Energizer, Nestle, etc. Executed projects for Banking Financial Companies to develop and validate different propensity models extensively.
Used Logit Model and Churn Model extensively to execute these models. Also used Cluster Analysis extensively. Maintained relationships with other functions/teams and coordinate with modeling consultants, consultants, and practice areas. Extensively used Multiple Regression Analysis and Logistic Regression analysis to run and model the data.
Used Logistic Regression analysis for the BFSI domain to help the client to focus on their target customer group and formulate their promotion strategy.
Also worked on Text analytics through R for one of the clients to help them understand their key customer concern areas. Expert in Base SAS, SAS/STAT SAS/Macro, MS Excel and extensively used SQL query, and Global Modeling Platform (GMP).
He has also been a corporate trainer in the field of analytics for the last 4 years. He has trained different companies like Genpact, HSBC, ITC, Wipro, etc. He has also trained more than 250 students (apart from the corporate training program) to coach them to learn and master Business Analytics.
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.
He is also associated with other institutes like Apponix, IVY Pro Scool, Edu Pristine, etc to impart 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:
- Data Mining: Extracting valuable insights from large datasets to inform decision-making.
- Statistical Analysis: Applying statistical methods to analyze data and identify trends.
- Predictive Modeling: Developing models to predict future outcomes based on historical data.
- Natural Language Processing (NLP): Utilizing algorithms to analyze and interpret human language.
- Computer Vision: Implementing techniques for image and video analysis.
Key Technologies:
- Visualization with Python: Creating compelling data visualizations to communicate insights effectively.
- Machine Learning with Python: Proficient in implementing 6 different algorithms to solve complex problems.
- Robotic Automation: Enhancing operational efficiency through automated processes.
- Multiple Linear Regression: Utilizing regression analysis for predictive analytics.
Student Success & Career Support: I have successfully trained over 1000 students at Apponix Academy, consistently achieving an average rating of 4.9. My dedicated career support includes:
- Resume Building: Guiding students to create standout resumes that attract employers.
- Interview Preparation: Offering personalized coaching and mock interviews to boost confidence.
- Job Placement Assistance: Helping students navigate the job market to secure desirable positions.
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.
- Cluster Analysis: Proficient in grouping data points to uncover patterns.
- SQL Queries (with Python): Experienced in data manipulation and analysis using SQL integrated with Python.
- Experimentation & A/B Testing: Knowledgeable in designing experiments to evaluate the effectiveness of changes.
- Time Series Analysis: Capable of analyzing temporal data to identify trends and seasonality.
- Cloud Computing (AWS, Azure, GCP): Utilizing cloud platforms for data storage and analytics.
- Database Management (SQL, NoSQL): Managing and querying relational and non-relational databases.
Student Success & Career Support:
I am dedicated to helping students succeed in their careers, having trained over 1500 students at Apponix Academy, with an impressive average rating of 4.9. My comprehensive career support includes:- Resume Building: Assisting students in creating impactful resumes tailored for data science roles.
- Interview Preparation: Conducting mock interviews and providing insights to boost confidence.
- Job Placement Assistance: Guiding students in securing positions in the competitive data science job market.
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:
- A/B Testing and Experimental Design: Crafting experiments to drive data-informed decisions.
- Data Engineering Skills (ETL Processes): Proficient in extracting, transforming, and loading data for analysis.
- Model Deployment and Productionization: Ensuring models are ready for real-world applications.
- Time Series Analysis: Analyzing data points collected or recorded at specific time intervals.
- Web Scraping Techniques: Extracting data from websites to gather insights.
Expertise:
- Computer Vision: Applying algorithms to interpret and process images and videos.
- Big Data Technologies: Proficient in tools like Hadoop and Spark for handling large datasets.
- Data Visualization: Transforming complex data into understandable visual formats.
- Data Wrangling and Cleaning: Ensuring data quality for accurate analysis.
Student Success & Career Support: I have trained over 1200 students at Apponix Academy, consistently receiving an average rating of 4.8. My comprehensive career support includes:
- Resume Building: Assisting students in creating impactful resumes tailored for data science roles.
- Interview Preparation: Conducting mock interviews and providing feedback to boost confidence.
- Job Placement Assistance:Guiding students in securing promising positions in the data science field.
Overview of Data Science Training Course in Los Angeles
This course follows a curriculum that covers all the major aspects of data science such as machine learning,data integration, data architecture, data visualization, predictive analytics, etc.We provide data science course in Los Angeles also.
Benefits of learning Data Science
Data science-related courses from Apponix will let you make the most out of the demand for skilled professionals in this sector so that you can land a high-paying job at your dream company.
With our data science certificate online or offline course, you will surely be able to have a competitive edge over other applicants interested to have a lucrative career in the field of data science.
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.
