“Data Science Training Course with Python for Data Analysis” helps the students to learn statistics, visualization, machine learning, and more with this comprehensive guide to practical data science with Python. This course provides a comprehensive manual on using Python for real data science. Therefore, if students take this course alone, they can avoid taking other courses or purchasing books on Python-based data science because it covers every facet of real data science.
Companies all over the world use Python to sort through the deluge of information at their disposal in this age of big data. Python may be used to store, filter, manage, and manipulate data, giving their business a competitive edge and catapulting your career to the next level. Python data science courses and books treat machine learning and data science as interchangeable concepts and fail to take into consideration the topic’s multidimensionality. Currently, udemy is offering the Data Science Training Course with Python for Data Analysis course for up to 87 % off i.e. INR 449 (INR 3,499).
Who can opt for this course?
- Anyone who wants to learn Python-based practical data science
- Anyone who is interested in learning how to use Python to implement machine learning algorithms
- People looking to start using Python for deep learning
- People looking to use Python to work with real-world data
- Anyone looking to expand into data analysis who has experience with Python
- Anyone who wants to learn how to use iPython to become proficient in exploratory data analysis, statistical modeling, and visualisation
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 12 Hours |
| Rating | 4.3/5 |
| Student Enrollment | 9,406 students |
| Instructor | Minerva Singh https://www.linkedin.com/in/minervasingh |
| Topics Covered |
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| Course Level | Beginner |
| Total Student Reviews | 1,757 |
Learning Outcomes
- Install Anaconda and work in the iPython/Jupyter environment for Python data analytics, a robust framework for data science analysis
- Learn How To Use The Most Popular Python Data Science Packages, Such As Numpy, Pandas, Scikit-Learn, and Matplotlib
- Be able to read data from many sources, including data from websites, and clean the data using data analysis techniques
- Utilize Python to do data exploration and pre-processing tasks including tabulation, pivoting, and data summarization
- Develop Your Skills Working With Real-World Data Collected From Various Sources
- Perform Data Visualization and learn when to use which techniques
- Use Python To Perform The Most Popular Statistical Data Analysis Techniques, Such As T-Tests & Linear Regression
- Recognize The Difference Between Statistical Data Analysis & Machine Learning
- Use real-world data and various unsupervised learning techniques
- Use supervised learning techniques on actual data, including classification and regression
- Review The Generality And Accuracy Of Machine Learning Models
- Construct fundamental neural networks and deep learning algorithms
- Implement Deep Neural Networks Using The Effective H2O Framework
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction to the Data Science in Python Bootcamp (01 hour 00 minutes) | What is Data Science? |
| Introduction to the Course & Instructor | ||
| Data For the Course | ||
| Introduction to the Python Data Science Tool | ||
| For Mac Users | ||
| Introduction to the Python Data Science Environment | ||
| Some Miscellaneous IPython Usage Facts | ||
| Online iPython Interpreter | ||
| Conclusion to Section 1 | ||
| 2. | Introduction to Python Pre-Requisites for Data Science (12 minutes) | Rationale Behind This Section |
| Different Types of Data Used in Statistical & ML Analysis | ||
| Different Types of Data are Used Programatically | ||
| Python Data Science Packages To Be Used | ||
| Conclusions to Section 2 | ||
| 3. | Introduction to Numpy (01 hour 10 minutes) | Numpy: Introduction |
| Create Numpy Arrays | ||
| Numpy Operations | ||
| Matrix Arithmetic and Linear Systems | ||
| Numpy for Basic Vector Arithmetic | ||
| Numpy for Basic Matrix Arithmetic | ||
| Broadcasting with Numpy | ||
| Solve Equations with Numpy | ||
| Numpy for Statistical Operation | ||
| Conclusion to Section 3 | ||
| Section 3 Quiz | ||
| 4. | Introduction to Pandas (40 minutes) | Data Structures in Python |
| Read in Data | ||
| Read in CSV Data Using Pandas | ||
| Read in Excel Data Using Pandas | ||
| Reading in JSON Data | ||
| Read in HTML Data | ||
| Conclusion to Section 4 | ||
| 5. | Data Pre-Processing/Wrangling (01 hour 36 minutes) | The rationale behind this section |
| Removing NAs/No Values From Our Data | ||
| Basic Data Handling: Starting with Conditional Data Selection | ||
| Drop Column/Row | ||
| Subset and Index Data | ||
| Basic Data Grouping Based on Qualitative Attributes | ||
| Crosstabulation | ||
| Reshaping | ||
| Pivoting | ||
| Rank and Sort Data | ||
| Concatenate | ||
| Merging and Joining Data Frames | ||
| Conclusion to Section 5 | ||
| 6. | Introduction to Data Visualizations (01 hour 29 minutes) | What is Data Visualization? |
| Some Theoretical Principles Behind Data Visualization | ||
| Histograms-Visualize the Distribution of Continuous Numerical Variables | ||
| Boxplots-Visualize the Distribution of Continuous Numerical Variables | ||
| Scatter Plot-Visualize the Relationship Between 2 Continuous Variables | ||
| Barplot | ||
| Pie Chart | ||
| Line Chart | ||
| Conclusions to Section 6 | ||
| 7. | Statistical Data Analysis-Basic (01 hour 16 minutes) | What is Statistical Data Analysis? |
| Some Pointers on Collecting Data for Statistical Studies | ||
| Some Pointers on Exploring Quantitative Data | ||
| Explore the Quantitative Data: Descriptive Statistics | ||
| Grouping & Summarizing Data by Categories | ||
| Visualize Descriptive Statistics-Boxplots | ||
| Common Terms Relating to Descriptive Statistics | ||
| Data Distribution- Normal Distribution | ||
| Check for Normal Distribution | ||
| Standard Normal Distribution and Z-scores | ||
| Confidence Interval-Theory | ||
| Confidence Interval-Calculation | ||
| Conclusions to Section 7 | ||
| 8. | Statistical Inference & Relationship Between Variables (01 hour 35 minutes) | What is Hypothesis Testing? |
| Test the Difference Between the Two Groups | ||
| Test the Difference Between More Than Two Groups | ||
| Explore the Relationship Between Two Quantitative Variables | ||
| Correlation Analysis | ||
| Linear Regression-Theory | ||
| Linear Regression-Implementation in Python | ||
| Conditions of Linear Regression | ||
| Conditions of Linear Regression-Check in Python | ||
| Polynomial Regression | ||
| GLM: Generalized Linear Model | ||
| Logistic Regression | ||
| Conclusions to Section 8 | ||
| Section 8 Quiz | ||
| 9. | Machine Learning for Data Science (11 minutes) | How is Machine Learning Different from Statistical Data Analysis? |
| What is Machine Learning (ML) About? Some Theoretical Pointers | ||
| 10. | Unsupervised Learning in Python (48 minutes) | Unsupervised Classification- Some Basic Ideas |
| KMeans-theory | ||
| KMeans-implementation on the iris data | ||
| Quantifying KMeans Clustering Performance | ||
| KMeans Clustering with Real Data | ||
| How Do We Select the Number of Clusters? | ||
| Hierarchical Clustering-theory | ||
| Hierarchical Clustering-practical | ||
| Principal Component Analysis (PCA)-Theory | ||
| Principal Component Analysis (PCA)-Practical Implementation | ||
| Conclusions to Section 10 | ||
| 11. | Supervised Learning (01 hour 38 minutes) | What is This Section About? |
| Data Preparation for Supervised Learning | ||
| Pointers on Evaluating the Accuracy of Classification and Regression Modelling | ||
| Using Logistic Regression as a Classification Model | ||
| RF-Classification | ||
| RF-Regression | ||
| SVM- Linear Classification | ||
| SVM- Non-Linear Classification | ||
| Support Vector Regression | ||
| knn-Classification | ||
| knn-Regression | ||
| Gradient Boosting-classification | ||
| Gradient Boosting-regression | ||
| Voting Classifier | ||
| Conclusions to Section 11 | ||
| Section 11 Quiz | ||
| 12. | Artificial Neural Networks (ANN) and Deep Learning (DL) (50 minutes) | Theory Behind ANN and DNN |
| Perceptrons for Binary Classification | ||
| Getting Started with ANN-binary classification | ||
| Multi-label classification with MLP | ||
| Regression with MLP | ||
| MLP with PCA on a Large Dataset | ||
| Start With Deep Neural Network (DNN) | ||
| Start with H20 | ||
| Default H2O Deep Learning Algorithm | ||
| Specify the Activation Function | ||
| H2O Deep Learning For Predictions | ||
| Conclusions to Section 12 | ||
| Section 12 Quiz | ||
| 13. | Miscellaneous Lectures & Information (25 minutes) | Data For This Section |
| Read in Data from Online CSV | ||
| Read Data from a Database | ||
| Data Imputation | ||
| Accessing GitHub |
Resources Required
- possess basic computer skills, including the ability to install programs
- An interest in learning data science
- Prior Python experience is helpful but not required
Featured Review
SR Reddi (5/5): It has been a superb learning experience going through the lectures of this course. it is a very valuable course which contains the latest information on the subject. It has immense opportunities for practical application.
Pros
- Hia Sumaiya (5/5): Best course materials I have ever seen! I have struggled to understand data analysis using python, and I almost gave up.
- Pavel Hoq (5/5): Best course ever! Details explained theory, exclusive resources, and well-decorated session makes this course awesome!
- Shikhar Agrawal (3/5): Any course needs practice assignments so that it can be understood perfectly.
- Yapsi Minsa (5/5): One of the best courses on these topics that I’ve taken till now.
Cons
- Vader S (2/5) : UPDATE: After a short pause and asking some pointed questions about some concepts I was disappointed in the engagement and delivery of the instructor.
- Srinivas Raghupatruni (1/5): This is not a good course for people who want to make a career in data science.
- Srinivas Raghupatruni (1/5): The instructor doesn’t explain the concepts but shows already written code and reads line by line which is not good.
- Abhijay Mishra (1/5): I have learned from some bad teachers but those teachers couldn’t teach properly.
About the Author
The instructor of this course is Minerva Singh who is a Bestselling Instructor & Data Scientist(Cambridge Uni) with a 4.4 instructor rating and 17,641 reviews on Udemy. Minerva Singh offers 49 Courses and has taught 89,647 Students so far.
- Minerva Singh finished her Ph.D. in 2017 at the University of Cambridge in the UK, where she concentrated on using data science tools to calculate the effects of forest loss on tropical ecosystems
- Minerva Singh graduated from Oxford University with an MPhil in the School of Geography and Environment and an MSc in the Department of Engineering
- Minerva Singh have more than ten years of experience conducting academic research (published in prestigious international peer-reviewed scientific journals like PLOS One) and offering advice to non-governmental and business stakeholders on matters relating to data science, deep learning, and earth observation (EO)
- Minerva Singh have a proven track record of using R and Python to accomplish machine learning, data visualization, spatial data analysis, deep learning, and NLP applications
- Minerva Singh has developed my statistical and data analysis skills through a variety of MOOCs, including The Analytics Edge (an R-based statistics and machine learning course offered by EdX), Statistical Learning (an R-based Machine Learning course offered by Stanford online), and the IBM Data Science Professional Certificate Track
- Minerva Singh has also received my education from some of the best universities in the world
- Minerva Singh have a wide range of specialties, including deep learning (Tensorflow, Keras), machine learning, spatial data analysis (including processing EO data), data visualization, natural language processing, and financial analysis, among others
- Minerva Singh has served as a peer reviewer for esteemed academic publications like Remote Sensing and delivered guest talks at illustrious gatherings like the Open Data Science Conference (ODSC)
Comparison Table
| Parameters | Complete Data Science Training with Python for Data Analysis | Data Science: Natural Language Processing (NLP) in Python | Natural Language Processing with Deep Learning in Python |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 13 hours | 12 hours | 12 hours |
| Rating | 4.3 /5 | 4.6 /5 | 4.6 /5 |
| Student Enrollments | 9,406 | 43,199 | 42,420 |
| Instructors | Minerva Singh | Lazy Programmer Inc. | Lazy Programmer Inc. |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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