I have Completed Full Stack Python in Appin Technology Lab. Teaching is very good and in understandable way. Staffs are very good and they easily interpret with us.
Learn how to apply fundamental programming concepts, computational thinking and data analysis techniques to solve real-world data science problems. Enroll For The Most Demanding Skill In The World, Data Science Training In Coimbatore From The Appin Technology Will Take Your Carreer To A New Height.
No wonder Appin Technology is regarded as the best Data Science training institute in Coimbatore to master Data Science concepts and crack a job.The online data science Course In Coimbatore program will help you learn in a structured way to gain in-depth knowledge and earn your Data Science Certification for a peak in your career. Our Data Science Course is effectively designed to understand the company requirements. We will prepare you to work as a professional Data Scientist, and we also provide a with 100 percent placement opportunities. Data Science Course in Coimbatore has been designed after consulting some of the best professionals in the industry. Are you keen on taking up a career in data science? Then contact the data science training institute in Coimbatore.
What you’ll learn From Data Science Training In Coimbatore
- Explore the data science process
- Probability and statistics in data science
- Data exploration and visualization
- Data ingestion, cleansing, and transformation
- Introduction to machine learning
- The hands-on elements of this course leverage a combination of R, Python.
- Data PreProcessing
- Data Imputation
- Data Cleaning
- Data Transformation
- Data Visualization
- Data Analysis
- Data Engineering – Big Data
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- Of these Data Analysis forms the chunk with a coverage of all Machine Learning algorithms – Regression, Clustering, Market Basket Analysis, Classification and Network Analysis and Recommendation Systems
- A programming language like R or Python and all above data task related packages are taught in such a program.
- Data Science is concerned with analyzing data and extracting useful knowledge from it.
- Building predictive models is usually the most important activity for a Data Scientist.
- However, because “Data Science” term is relatively new, the name is not commonly accepted yet, and other names are frequently used for the same area.It is also concerned with Data Visualization and presenting results in the form understandable to people.
Why Appin Technology Lab For Data Science Course In Coimbatore ?
- Appin Technology is an Leading Training Institute Providing best Data Science training in Coimbatore.
- This course is a great introduction to both fundamental programming concepts and the Data Science programming
- By the end, you’ll be familiar with Data Science and you’ll be able to put into practice what you’ll have learned in a final project you’ll develop locally.
- Appin Coimbatore has experience Data Science Developer who can teach this language with Project oriented coaching.
- Appin Coimbatore offers 100% placement assistance And Also Has Nominal Fees
- See Our Google Review For More Clarity About Our Company
- Appin Coimbatore provides the best Data Science Course in Coimbatore to help the beginners to enhance a good foundation in the programming languages.
Data Science Syllabus
Statistics Essentials for Analytics
- Understanding the Data
- Probability and its Uses
- Statistical Inference
- Data Clustering
- Testing the Data
- Regression Modelling
Data Science Overview
- Data Science
- Data Scientists
- Examples of Data Science
- Python for Data Science
Data Analytics Overview
- Introduction to Data Visualization
- Processes in Data Science
- Data Wrangling, Data Exploration, and Model Selection
- Exploratory Data Analysis or EDA
- Data Visualization
- Plotting
- Hypothesis Building and Testing
Statistical Analysis and Business Applications
- Introduction to Statistics
- Statistical and Non-Statistical Analysis
- Some Common Terms Used in Statistics
- Data Distribution: Central Tendency, Percentiles, Dispersion
- Histogram
- Bell Curve
- Hypothesis Testing
- Chi-Square Test
- Correlation Matrix
- Inferential Statistics
Data Visualization in Python using Matplotlib
- Introduction to Data Visualization
- Python Libraries
- Plots
- Matplotlib Features:
- Line Properties Plot with (x, y)ü
- Controlling Line Patterns and Colorsü
- Set Axis, Labels, and Legend Propertiesü
- Alpha and Annotationü
- Multiple Plotsü
- Subplotsü
- Types of Plots and Seaborn
Python: Environment Setup and Essentials
- Introduction to Anaconda
- Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
- Jupyter Notebook Installation
- Jupyter Notebook Introduction
- Variable Assignment
- Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
- Creating, accessing, and slicing tuples
- Creating, accessing, and slicing lists
- Creating, viewing, accessing, and modifying dicts
- Creating and using operations on sets
- Basic Operators: ‘in’, ‘+’, ‘*’
- Functions
- Control Flow
Mathematical Computing with Python (NumPy)
- NumPy Overview
- Properties, Purpose, and Types of ndarray
- Class and Attributes of ndarray Object
- Basic Operations: Concept and Examples
- Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
- Copy and Views
- Universal Functions (ufunc)
- Shape Manipulation
- Broadcasting
- Linear Algebra
Scientific computing with Python (Scipy)
- SciPy and its Characteristics
- SciPy sub-packages
- SciPy sub-packages –Integration
- SciPy sub-packages – Optimize
- Linear Algebra
- SciPy sub-packages – Statistics
- SciPy sub-packages – Weave
- SciPy sub-packages – I O
Data Science with Python Web Scraping
- Web Scraping
- Common Data/Page Formats on The Web
- The Parser
- Importance of Objects
- Understanding the Tree
- Searching the Tree
- Navigating options
- Modifying the Tree
- Parsing Only Part of the Document
- Printing and Formatting
- Encoding
Data Manipulation with Python (Pandas)
- Introduction to Pandas
- Data Structures
- Series
- DataFrame
- Missing Values
- Data Operations
- Data Standardization
- Pandas File Read and Write Support
- SQL Operation
Machine Learning with Python (Scikit–Learn)
- Introduction to Machine Learning
- Machine Learning Approach
- How Supervised and Unsupervised Learning Models Work
- Scikit-Learn
- Supervised Learning Models – Linear Regression
- Supervised Learning Models: Logistic Regression
- K Nearest Neighbors (K-NN) Model
- Unsupervised Learning Models: Clustering
- Unsupervised Learning Models: Dimensionality Reduction
- Pipeline
- Model Persistence
- Model Evaluation – Metric Functions
Natural Language Processing with Scikit-Learn
- NLP Overview
- NLP Approach for Text Data
- NLP Environment Setup
- NLP Sentence analysis
- NLP Applications
- Major NLP Libraries
- Scikit-Learn Approach
- Scikit – Learn Approach Built – in Modules
- Scikit – Learn Approach Feature Extraction
- Bag of Words
- Extraction Considerations
- Scikit – Learn Approach Model Training
- Scikit – Learn Grid Search and Multiple Parameters
- Pipeline
Python integration with Hadoop, MapReduce and Spark
- Need for Integrating Python with Hadoop
- Big Data Hadoop Architecture
- MapReduce
- ClouderaQuickStart VM Set Up
- Apache Spark
- Resilient Distributed Systems (RDD)
- PySpark
- Spark Tools
- PySpark Integration with Jupyter Notebook
Trainer Profile
At Appin Coimbatore, we take pride in our team of highly qualified and experienced trainers who are dedicated to providing quality education in Data Science with Python. Here is a glimpse into the profile of our trainers:
Industry Experience : Our trainers are industry professionals with extensive experience in the field of Data Science and Python programming. They have worked on real-world projects, gained practical knowledge, and understand the challenges and trends of the industry.
Subject Matter Experts : Our trainers are subject matter experts in Data Science and Python. They have in-depth knowledge of the concepts, tools, and techniques used in Data Science projects.
Teaching Experience : Our trainers have a strong background in teaching and have honed their instructional skills over the years.
Updated with Latest Trends : Our trainers stay abreast of the latest trends and advancements in Data Science and Python. They continuously update their knowledge and skills to incorporate emerging technologies, industry practices, and new developments into the training curriculum.
Mentors and Guides : Our trainers go beyond just delivering lectures. They act as mentors and guides, providing individual attention and support to each participant.
Career Advancement : Data Science with Python certifications can significantly enhance your career prospects. Employers often prefer candidates with certifications as it provides assurance of their competency in utilizing Python for data analysis, machine learning, and other data science tasks.
Upcoming Events
Free Python Demo
The Python Training event at Appin Technology is scheduled from March 18. Don’t miss out on this incredible opportunity.
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Free Digital Marketing Workshop
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Free Career Enhancement Program
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Data Science With Python Course FAQ
Data Science involves extracting insights and knowledge from large datasets to drive informed decision-making. Python is popular in Data Science due to its simplicity, readability, extensive libraries (such as Pandas, NumPy, and scikit-learn), and its ability to handle data manipulation, analysis, and visualization tasks efficiently.
While prior programming experience can be helpful, it is not always necessary. Python is considered a beginner-friendly language, and many Data Science courses and training programs cater to individuals with various levels of programming experience.
Basic knowledge of programming concepts, such as variables, loops, conditionals, and functions, is beneficial. Familiarity with mathematical and statistical concepts is also useful, as Data Science involves analyzing and interpreting data using mathematical and statistical techniques.
Data Science with Python has a wide range of applications, including but not limited to: predictive analytics, customer segmentation, recommendation systems, fraud detection, sentiment analysis, image recognition, natural language processing, and time series forecasting.
The time required to learn Data Science with Python depends on factors such as your prior programming experience, the intensity of the learning program, and the depth of knowledge you wish to acquire. Generally, it can take several months to a year to develop a solid foundation in Data Science and Python.