If you are searching for the best training on Data science with Python in Bangalore, you have found the correct place, Upshot technologies in BTM, Bangalore. Because, we are the Best training centre in teaching Data science with Python in Bangalore.
About Data science:
How to extract insights from Data which includes structured and unstructured data.
Unifies statistics, data analysis and their related methods to analyze data.
Uses techniques and ideas from various fields such as mathematics, statistics, and computer science (Databases, machine learning etc).
Used commonly in business analytics only because other alternatives like Big Data and Machine learning had grown enormously in recent years.
Popular high-level general-purpose programming language.
Created by Guido van Rossum and released in 1991.
Open source and community based development.
Its two important features are code readability and the syntax.
Code readability means the use of white space in the place of braces.
Its syntax needs fewer lines of code than all other programming languages.
Used in various domains such as web applications, internet scripting, database and gaming.
Upshot Technologies is the No. 1 training institute in Bangalore and providing the best training in Data Science with Python. Some of the advantages of joining the best training institute are given below:
Crafted to provide the bests of Data science and Python to our students.
Tailored to fulfill the expectations of the IT industry.
Comprehensive enough to master both Data science and Python.
Prepared by a team of experts who also prepared the study materials.
Includes many case studies and real-life examples.
Professionals with maximum expertise and 10+ years of work experience.
Have immense knowledge in both Data science and Python.
Kind and helping teachers who prioritizes the education of students over comfort.
Clears the doubts of students after every class or at the earliest possibility available.
Provides counselling and feedbacks to our students whenever needed.
Up-to-date computer lab with Python and other Data science tools required.
Projector-friendly smart classrooms and spacious and calm study halls.
Video-conferencing enabled classrooms to conduct webinars and guest lectures.
Free High-speed Internet to encourage our students learn more.
100% placement record in all the years till now.
Our placement cell work hard to ensure that all of our students are placed.
Help to prepare a remarkable Resume.
Provide a lot of study materials for interview preparation.
Conduct a lot of mock tests and interviews to boost the confidence of our students.
There are also other benefits in choosing the Best training institute to learn Data science with Python such as
Various batch timings to admit students, to-be employed and employed professionals.
Flexible fees structure and payment methods to help the needy students.
Access to an online library containing information about Data science and Python.
1-to-1 training and online training can be arranged if informed earlier.
Data Science with Python Course Syllabus
Lesson 1: Data Science Overview
- Data Science
- Data Scientists
- Examples of Data Science
- Python for Data Science
Lesson 2: 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
- Hypothesis Building and Testing
Lesson 3: 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
- Bell Curve
- Hypothesis Testing
- Chi-Square Test
- Correlation Matrix
- Inferential Statistics
Lesson 4: 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’, ‘+’, ‘*’
- Control Flow
Lesson 5: 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
- Linear Algebra
Lesson 6: 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
Lesson 7: Data Manipulation with Python (Pandas)
- Introduction to Pandas
- Data Structures
- Missing Values
- Data Operations
- Data Standardization
- Pandas File Read and Write Support
- SQL Operation
Lesson 8: Machine Learning with Python (Scikit–Learn)
- Introduction to Machine Learning
- Machine Learning Approach
- How Supervised and Unsupervised Learning Models Work
- 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
- Model Persistence
- Model Evaluation – Metric Functions
Lesson 9: 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
Lesson 10: Data Visualization in Python using Matplotlib
- Introduction to Data Visualization
- Python Libraries
- Matplotlib Features:
- Line Properties Plot with (x, y)
- Controlling Line Patterns and Colors
- Set Axis, Labels, and Legend Properties
- Alpha and Annotation
- Multiple Plots
- Types of Plots and Seaborn
Lesson 11: 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
Lesson 12: Python integration with Hadoop, MapReduce and Spark
- Need for Integrating Python with Hadoop
- Big Data Hadoop Architecture
- Cloudera QuickStart VM Set Up
- Apache Spark
- Resilient Distributed Systems (RDD)
- Spark Tools
- PySpark Integration with Jupyter Notebook
There is no Official certification for Data Science with Python but you have separate certifications for Data science. For Python till now there are no official certifications but the process to introduce official certifications has started. We will guide you throughout the process to get the specific Data science certification you prefer and for Python, our course completion certificate along with your own Python program which you did in the practical sessions is more than enough. However, these certifications are not mandatory to get a job because you will be placed in a company of your liking as soon as you had completed our course on Data science with Python
After the completion of our training on Data science with R, you will have numerous job opportunities from all over the world. Some of the positions you will be appointed to, are listed below:
Apart from these, there are other additional benefits such as promotions, switching job to a MNC and teaching Data science or Python at institutes or online platforms based on your availability.
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