DATA SCIENCE USING PYTHON
Data Science using Python, Learn Data Science using Python course know more about the data scientists in Python course, NLP, deep learning with online training course provided by Mildaintrainings, learn web scraping, business analysis, supervised learning, artificial intelligence, and machine learning This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
4 Days / 32 Hrs
For Classroom & Online Training
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Email Id : info@mildaintrainings.com
Contact no: +91 8447121833
Data Science using Python
MODES OF TRAINING
ONLINE CLASSROOM
Instructor-Led Training
Live Online Classroom
ONE TO ONE
Instructor-Led Training
Live Online Classroom
TEAM / CORPORATE
Train Your Team
And Up-Skill Them
Data Science using Python
Price: USD 499
Course Outline: Click here
Discounts: We offer multiple discount options for team and corporates call or whatsapp+91-8447121833 for more info.
Placement: 100% Placement Assistance
Delivery Options: Attend remote-live or on-demand online classes.
Neural Network in Python & R
DESCRIPTION
DESCRIPTION
Artificial Neural Network ANN
This Data Science with python training encompasses basic statistical concepts to advanced analytics and predictive modelling techniques using Python, along with machine learning using Python.
This course is designed considering some specific industry segments where Python is still one of the most important analytics tools which is preferred for reporting analytics and predictive modelling, while python gets an edge when it comes advance data science and machine learning applications.
Crafted and delivered by a team of industry experts, this comprehensive Python data science training has all the components required to give you a head-start into the field of advance Analytics!
Python Data Science course duration: 32-40 hours.
Course Objectives
The Python for Data Science course is packed with real-life projects focused on customer segmentation, macro calls, attrition analysis, and retail analysis, as well as demos and case studies to give you practical experience in installing and working in the Python environment.
Python has surpassed Java as the top language used to introduce US students to programming and computer science, and 46 percent of data science jobs list Python as a required skill.
Course Prerequisites
There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science with Python training particularly for the following professionals:
- Analytics professionals who want to work with Python
- Software professionals looking to get into the field of analytics
- IT professionals interested in pursuing a career in analytics
- Graduates looking to build a career in analytics and data science
- Experienced professionals who would like to harness data science in their fields
- Anyone with a genuine interest in the field of data science
Prerequisites: There are no prerequisites for this Data Science with Python course. The Python basics course included with this program provides additional coding guidance.
CURRICULUM
CURRICULUM
Data Science with Python
- Course Overview
- Data Science Overview
- Introduction to Data Science
- Different Sectors Using Data Science
- Purpose and Components of Python
Data Analytics Overview
- Data Analytics Process
- Knowledge Check
- Exploratory Data Analysis(EDA)
- EDA-Quantitative Technique
- EDA – Graphical Technique
- Data Analytics Conclusion or Predictions
- Data Analytics Communication
- Data Types for Plotting
- Data Types and Plotting
Statistical Analysis and Business Applications
- Introduction to Statistics
- Statistical and Non-statistical Analysis
- Major Categories of Statistics
- Statistical Analysis Considerations
- Population and Sample
- Statistical Analysis Process
- Data Distribution
- Dispersion
- Histogram
- Testing
- Correlation and Inferential Statistics
Python Environment Setup and Essentials
- Anaconda
- Installation of Anaconda Python Distribution (contd.)
- Data Types with Python
- Basic Operators and Functions
Mathematical Computing with Python (NumPy)
- Introduction to Numpy
- Activity-Sequence it Right
- Creating and Printing an ndarray
- Knowledge Check
- Class and Attributes of ndarray
- Basic Operations
- Activity-Slice It
- Copy and Views
- Mathematical Functions of Numpy
Scientific computing with Python (Scipy)
- Introduction to SciPy
- SciPy Sub Package – Integration and Optimization
- SciPy sub package
- Calculate Eigenvalues and Eigenvector
- SciPy Sub Package – Statistics, Weave and IO
Data Manipulation with Pandas
- Introduction to Pandas
- Understanding DataFrame
- View and Select Data Demo
- Missing Values
- Data Operations
- Knowledge Check
- File Read and Write SupportPreview
- Knowledge Check-Sequence it Right
- Pandas Sql Operation
Machine Learning with Scikit–Learn
- Machine Learning Approach
- How it Works
- Supervised Learning Model Considerations
- Scikit-Learn
- Supervised Learning Models – Linear Regression
- Supervised Learning Models – Logistic Regression
- Unsupervised Learning Models
- Pipeline
- Model Persistence and Evaluation
Natural Language Processing with Scikit Learn
- NLP Overview
- NLP Applications
- Knowledge check
- NLP Libraries-Scikit
- Extraction Considerations
- Scikit Learn-Model Training and Grid Search
Data Visualization in Python using matplotlib
- Introduction to Data Visualization
- Knowledge Check
- Line Properties
- (x,y) Plot and Subplots
- Knowledge Check
- Types of Plots
Web Scraping with BeautifulSoup
- Web Scraping and Parsing
- Knowledge Check
- Understanding and Searching the Tree
- Navigating options
- Navigating a Tree
- Modifying the Tree
- Parsing and Printing the Document
Python integration with Hadoop MapReduce and Spark
- Why Big Data Solutions are Provided for Python
- Hadoop Core Components
- Python Integration with HDFS using Hadoop Streaming
- Python Integration with Spark using PySpark
FAQ | Data Science using Python
Why should I take Data Science using Python Training from Mildaintrainings?
You should go for Data Science using Python from Mildaintrainings as our trainers have 12 plus years of industry practical experience and we also provide Practical training with the live project so that you could understand each and everything better, it will help you in your job. At Mildaintraining we will provide you six months Technical support as well.
Do I get the Data Science using Python training certificate?
Yes, at Mildaintrainings we will provide you participation certificate after the completion of Data Science using Python course from Mildaintrainings.
When will the classes be held for Data Science using Python?
Classes will be held on weekends as well as weekdays as per schedule or your convenience.
What if I miss the Data Science using Python class?
If you miss the class in that case backup class can be adjusted in next live session.
What is Data Science using Python course duration?
This course duration will be of 32 – 40 hours or 4 days and it will be Instructor lead training at Mildaintrainings with Practical training with live project. The timing will be according to your convenience it can be on weekend and weekdays.
Reviews
Rekha Amminbhavi
Testing Engineer at CSC
Guidewire The program was really knowledgeable
and the modules were just perfectly made and managed
and were taught with ease.
I am so happy I choose mildain
Jyotish Phukon
Senior EDI Analyst at SIQES
IBM Sterling Integrator Good training. Explaining the things with practical examples. Well experienced and confident enough to answer every query. Trainers are working more as a friend rather than working like for money. Worth of paying for the course.
Machine Leaning With Python
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