Data Science Using Python Hot & new
Unleash data-driven insights with Python's advanced analytics toolkit.
By Von wick
Last update 10 Sep. 2019
This course includes
- 48.5 hours on-demand video
- 57 articles
- 12 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
Description
Master data manipulation, visualization, and predictive modeling techniques with Python. This course bridges the gap between theory and practice, empowering you to solve complex data problems.
Objective of the Course
- Comprehensive coverage of Python libraries like NumPy, Matplotlib, and Seaborn.
- Develop skills in data visualization, statistical analysis, and machine learning.
- Industry-relevant projects to boost your portfolio.
Machine Learning Data Science 09 lectures
- Lecture1.1 Key Elements of Machine Learning & Data Science & differences between them
- Lecture1.2 Data Warehousing
- Lecture1.3 Business Intelligence
- Lecture1.4 Data Visualization
- Lecture1.5 Data Mining
- Lecture1.6 Machine Learning
- Lecture1.7 Artificial Intelligence
- Lecture1.8 Cloud Computing
- Lecture1.9 Big Data
Introduction to MACHINE LEARNING 06 lectures
Python Language 08 lectures
- Lecture3.1 Operators, Operands and Expressions
- Lecture3.2 Python Data Types
- Lecture3.3 Conditional statements in Python
- Lecture3.4 Loops in Python
- Lecture3.5 Lists and dictionaries and Tuples
- Lecture3.6 Programming practice in Python
- Lecture3.7 Iterators & Generators
- Lecture3.8 File Handling in Python
- Lecture3.9 Modules and Libraries
- Lecture3.10 Classes and Objects
- Lecture3.11 String Formatting in Python
- Lecture3.12 Decorators, Context Managers, Regular Expressions
- Lecture3.13 List and Dictionary Comprehensions
- Lecture3.14 Lambda and Argument Passing
- Lecture3.15 Multiple Inheritance
Probability and Statistics 04 lectures
NUMPY, PANDAS, Matplotlib, Sk-learn, seaborn 011 lectures
- Lecture5.1 Introduction to Numpy
- Lecture5.2 Arrays, Matrices
- Lecture5.3 Various operations on arrays and matrices
- Lecture5.4 Introduction to Pandas
- Lecture5.5 Reading csv and matlab files
- Lecture5.6 Data frame object manipulation in python
- Lecture5.7 Various operations on data frame
- Lecture5.8 Visualization using Matplotlib
- Lecture5.9 Scatter plots, line plots etc on a given data
- Lecture5.10 Advance visualization using Seaborn
- Lecture5.11 Histograms, heatmaps, box plots etc using seaborn
Data Processing - Machine Learning 022 lectures
- Lecture6.1 Basic Functionalities of a data object
- Lecture6.2 Merging of Data objects
- Lecture6.3 Concatenation of data objects
- Lecture6.4 Types of Joins on data objects
- Lecture6.5 Exploring a Dataset
- Lecture6.6 Analysing a dataset
- Lecture6.7 Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
- Lecture6.8 GroupBy operations
- Lecture6.9 Aggregation
- Lecture6.10 Concatenation
- Lecture6.11 Merging
- Lecture6.12 Joining
- Lecture6.13 Data Collection &Preparation
- Lecture6.14 Data Mugging
- Lecture6.15 Outlier Analysis
- Lecture6.16 Missing value treatment
- Lecture6.17 Feature Engineering
- Lecture6.18 Data Transformation
- Lecture6.19 Normalization vs Standardization
- Lecture6.20 Creating Dummies
- Lecture6.21 Dimensionality Reduction
- Lecture6.22 Principal ComponentAnalysis
Data Science and Machine Learning - Statistics 012 lectures
- Lecture7.1 Confidence Interval
- Lecture7.2 Student’s t distribution
- Lecture7.3 Binomial Distribution
- Lecture7.4 A/B Testing
- Lecture7.5 Hypothesis Testing
- Lecture7.6 t-Tests
- Lecture7.7 ANOVA
- Lecture7.8 Chi-square test
- Lecture7.9 KNN
- Lecture7.10 PCA
- Lecture7.11 Categorical Variables
- Lecture7.12 R Square
Data Science and Machine Learning - Statistics 012 lectures
- Lecture7.1 Confidence Interval
- Lecture7.2 Student’s t distribution
- Lecture7.3 Binomial Distribution
- Lecture7.4 A/B Testing
- Lecture7.5 Hypothesis Testing
- Lecture7.6 t-Tests
- Lecture7.7 ANOVA
- Lecture7.8 Chi-square test
- Lecture7.9 KNN
- Lecture7.10 PCA
- Lecture7.11 Categorical Variables
- Lecture7.12 R Square
Regression Modelling 06 lectures
Machine Learning Algorithms ( Advanced ) 024 lectures
- Lecture9.1 Supervised Machine Learning algorithms
- Lecture9.2 Linear Regression
- Lecture9.3 Multi Feature
- Lecture9.4 Logistic Regression
- Lecture9.5 2 Class and Multi class
- Lecture9.6 Decision/ Classification
- Lecture9.7 Trees
- Lecture9.8 Ensemble
- Lecture9.9 Models
- Lecture9.10 Bagging
- Lecture9.11 Boosting
- Lecture9.12 Random Forest
- Lecture9.13 K-Nearest Neighbours (KNN)
- Lecture9.14 Naive Bayes
- Lecture9.15 Introduction to Neural Network (DeepLearning)
- Lecture9.16 Feed Forward Neural Network
- Lecture9.17 Forward Propagation
- Lecture9.18 Backward Propagation
- Lecture9.19 Support Vector Machine
- Lecture9.20 Unsupervised Machine Learning algorithms
- Lecture9.21 Clustering with K-means Clustering
- Lecture9.22 Bias-Variance Tradeof
- Lecture9.23 Regularization
- Lecture9.24 Parameter tuning & grid search optimization
Projects and Assignments 02 lectures
Job & Placement Details
- Strong demand in data-driven industries like finance, healthcare, and e-commerce.
- Partnership with leading data science firms for placements.
- Job Roles: Data Scientist, Business Analyst, Data Engineer
Kosmi Kotalia
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injected humour, or randomised words which.
ReplyAdam Wood
Lorem ipsum, dolor sit amet consectetur adipisicing elit. Maxime ratione libero temporibus quo id iste, esse at accusamus atque qui.
ReplyLina Handshake
Asperiores minus, laborum incidunt explicabo dolore officiis praesentium veniam expedita libero harum pariatur reiciendis porro debitis aliquam quas labore ducimus nisi. Blanditiis?
Reply