Course Duration
Live Projects
Training Format
Python With Data Science Professionals Trained
New Batches Every Month
Industry Experience
Corporates Worked with
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Python With Data Science Overview
UpScale's Core Java Training enables you to master the complete Python With Data Science
Modes of Training
SELF PACED LEARNING
- High Quality videos built by industry experts with theory and demonstration of features and tasks of the Selenium with Java
- Learn at your Convenience.
- You get pre-defined recordings
- Delivered through LMS.
- Fixed Course Content.
- Certification Guidance Provided.
SELF PACED LEARNING
- High Quality videos built by industry experts with theory and demonstration of features and tasks of the Selenium with Java
- Learn at your Convenience.
- You get pre-defined recordings
- Delivered through LMS.
- Fixed Course Content.
- Certification Guidance Provided.
Curriculum
What is analytics & Data Science?p>
Need of Data Science In the Industry
Analytics vs. Data warehousingp>
OLAP, MIS Reporting
Overview of analytics tools & their popularity
Analytics Methodology & problem solving framework
Why Python for data science?
Starting with Python
Introduction to installation of Python
Introduction to Python Editors & IDE's
Concept of Packages/Libraries - Important packages
List and Dictionary Comprehensions
Basic Operations - Mathematical - string - date
Numpy
Scify
Pandas
Scikitlearn
Statmodels
Nltk
Importing Data from various sources
Database Input (Connecting to database)
Viewing Data objects - subsetting, methods
Exporting Data to various formats
Important python modules: Pandas, beautifulsoup
Cleansing Data with Python
Data Manipulation steps
Data manipulation tools
Python Built-in Functions
Python User Defined Functions
Stripping out extraneous information
Normalizing and Formatting of Data
Important Python modules for data manipulation
Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis
Creating Graphs
Important Packages for Exploratory Analysis
Basic of Statistics
Measures of Central Tendencies and Variance
Probability Distributions
Normal distribution
Central Limit Theorem
Inferential Statistics
Statistical Methods - Z/t-tests
Concept of model in analytics and how it is used?
Common terminology used in analytics & modelling process
Popular modelling algorithms
Mapping of Techniques
Different Phases of Predictive Modelling
Need of Data preparation
Consolidation/Aggregation
Outlier treatment
Flat Liners
Missing values
Missing values
Dummy creation
Variable Reduction
Variable Reduction Techniques
Introduction to Segmentation
Types of Segmentation
Sedmentation Techniques
Cluster evaluation and profiling
Linear Regression
Logistic Regressions
Solving Regression Problem
Solving Classification Problem
Building Linear Regression Model
Understanding standard metrics
Validation of Models
Standard Business Outputs
Time Series Compon
Classification of Techniques
Basic Techniques
Advanced Techniques
Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc.
Projects