Data Science with Python
Boost your career with Python Data Science Program. An industry leader offers world-class Data Science training on the most in-demand Data Science skills. Gain hands-on experience with key technologies such as Python and Machine Learning. Become an expert in Data Science today.
The program covers a wide range of topics, from Python to Exploratory Data Analysis to Machine Learning and Deep Learning and more. Our instructors and assistants supervise you while you complete the coursework, using practical labs to bring these ideas to life.
What’s in it for me?
Upon completion of this program, you will:
1) Be familiar with analytics tools and technologies such as Python
2) Apply industry-relevant machine learning techniques such as regression, predictive modeling, clustering, time series forecasting, classification, etc.
3) Use statistics and data modeling to build an analytics framework for a business problem
4) Perform using data cleaning and data transformation operations several tools and techniques
5) Be well versed in Deep learning, Natural Language Processing (NLP).
6) Present yourself to leading analytics companies as an ideal candidate for analyst, data engineer, and data scientist roles
Role Offered
1) Data Scientist
2) Manager Analytics
3) ML Engineer
4) AI Engineer
5) Reporting Analyst
6) Research Executive
This course offers Data Science with Python Certification Validation Tool for Employers
Employers, clients, and other stakeholders can use your Data Science Certification Validation Tool to check the authenticity of your certification.
The Data Science with Python @ The Learnify academy:
Why The Learnify academy
The Data Science with Python program @ The Learnify academy:
1) A focus on learning, understanding, and implementing concepts rather than merely gaining theoretical knowledge
2) Interactive classes and small batch sizes
3) Facilitated by an industry expert with over ten years of experience
4) Instructor-led interactive virtual classroom sessions for 40+ hours on weekends
5) Support for six months following completion of training, i.e. monthly revision classes
6) Certificates are valid for a lifetime
7) Session recordings are available for life
8) 15:1 participant-faculty ratio
9) Four or more capstone projects
Course Curriculum:
| Course Curriculum - Data Science with Python |
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| Data Science Overview | Data Manipulation with Pandas | Machine Learning – Supervised & Unsupervised |
| # DS Spectrum | # File read and write support | # Supervised VS Unsupervised |
| # S use cases (Different sectors using) | # Understanding Data frame and Series | # Linear & Logistic Regression |
| # Difference b/w AI, ML, DL & DS | # Data Operations | # Decision Tree & Random Forest |
| Introduction to Python and Anaconda (Python setup) | # Group by & Aggregation | # Naive Bayes & Support Vector Machine |
| # Why Python | # Join/Merge/Concatenation | # Boosting – Xgboost, Adaboost & others boosting algorithm |
| # Anaconda Installation | # Industry Use Cases | # KNN |
| # Why Jupyter notebook | Visualization in Python using Matplotlib and Seaborn | # Principal Component Analysis (PCA) |
| Python Basics and Exception Handling | # Different Plots – Scatter, Histogram, Bar chart, Pie chart, Stacked, column chart, Pair plot, density plot, Line chart, and Violin Plot | # K- Means Clustering & DBSCAN Clustering |
| Python as a calculator | # Outlier Analysis using Box Plot | NLP – Natural Language Processing |
| # Data types with Python – Variables, Objects | # Industry Use Cases | # Text Preprocessing, Wordcloud |
| # Operators – Comparison, Logical & Arithmetic | Statistics essential for Data Science | # Sentiment Analysis |
| # Conditional Statement – If , elif & else | # Introduction | # Text Classification use case |
| # Loops – While, for | # Sampling and Population | Deep Learning – Basics |
| # Functions – Python inbuilt & User-defined function | # Measures of Central tendency, Dispersion, Skewness, and Kurtosis | # Introduction to Artificial Neural Network (ANN) |
| # OOPS (Object Oriented Programming) Concept in Python | # Karl Pearson and Spearman Correlation | Four End to End ML Industry based Project |
| # Exception Handling in Python | # Discrete and Continuous Distributions | # Supervised Classification Project |
| Mathematical computing with Python (Numpy) | # Inferential Statistics and Confidence Interval | # Supervised Regression Project |
| # One/Two/Three Dimensional array | # Hypothesis Testing – Large and Small sample tests | # Unsupervised Clustering Project |
| # Array Operations | # Non-Parametric Tests | # NLP – Movie review Project |
| # Industry Use Cases | ||
