Python is a very popular general purpose, high level programming language which can be used for web development, software development, mathematics, system scripting etc. At Opine, we use complete hands-on-approach, provides lots of classroom and home assignments, teach basic mathematical and statistical concepts required which makes our students fully equipped for diving in the job market.
Accomplish multi-step tasks like sorting or looping using tuples
Create programs that are able to read and write data from files
Store data as key/value pairs using Python dictionaires
Use variables to store, retrieve and calculate information
Hours: 50
| Sr. No. | Topic | Description |
|---|---|---|
| 1 | Setting Up Python in Windows | Introduction to Different Environments and Notebooks |
| 2 | Numerators, Operators and Comments and Boolean | The difference between = and IS |
| 3 | Data Types | String Concatenation, Changing Data Types |
| 4 | Conditional Logic | If-Else, Working with cases, the first python game |
| 5 | Loops - While and For | Allowing the user to play the game as many times as they want |
| 6 | Lists | Indexing, methods and storage |
| 7 | Dictionaries | Indexing, methods and storage |
| 8 | Tuples | Indexing, methods and storage |
| 9 | Functions | Developing your own functions and working with in-built functions |
| 10 | Debugging and Error Handling | Difference between error in logic and syntactical errors |
| 11 | Object Oriented Programming | Objects and working with them |
| 12 | Iterators and Generators | Working with Loops, Creating our own version of loops |
| 13 | Reg-Ex | Wildcards |
| 14 | SQL embedding in Python | Basic SQL and connections |
Hours: 35
| Sr. No. | Topic | Description |
|---|---|---|
| 1 | Setting Up Jupyter notebook | |
| 2 | Numpy | Arrays, Indexing in Arrays |
| 3 | Pandas | Data Transformations, Working With Data |
| 4 | Working With Data – 1 | JSON, HTML and Excel Data |
| 5 | Working With Data – 2 | Data Frames, Index, Outliers, Merging, Aggregations, Grouping Data Frames |
| 6 | SciKit | Introduction to ML |
| 7 | Statistics – Concepts | Descriptive Statistics (Variances, Standard Deviations, Co-variances, Bi-Variate Regression (Linear), Multi-Variate Regression (Linear) |
| 8 | Linear Regression | Python Application |
| 9 | Multiclass Classificaion – 1 | Logistic Regression |
| 10 | Multiclass Classificaion – 2 | k - Nearest Neighbour |
| 11 | Vector Machines | Supervised Learning Models |
| 12 | Naïve Bayes | Probabilistic Classifiers and Maximum Likelihood |
| 13 | Decision Trees and Ranom Forest | Classifications and Randomised Forests |
| 14 | Introduction To Keras, TensorFlow | Randomised Machine Learning Algorithms |
Hours: 30
| Sr. No. | Topic | Description |
|---|---|---|
| 1 | Setting Up Jupyter notebook | |
| 2 | Numpy | Arrays, Indexing in Arrays |
| 3 | Pandas | Data Transformations, Working With Data |
| 4 | Working With Data – 1 | JSON, HTML and Excel Data |
| Working With Data – 2 | Data Frames, Index, Outliers, Merging, Aggregations, Grouping Data Frames | |
| 5 | Seaborn | Understanding the Wrapper |
| 6 | Histogram | Visualisation Objects |
| 7 | Kernel Density Estimate Plots | Visualisation Objects |
| 8 | Density plots | Visualisation Objects |
| 9 | Charts | Visualisation Objects |
| 10 | Regression Plots | Visualisation Objects |
| 11 | Heat-maps | Visualisation Objects |
| 12 | Clustered Matrices | Visualisation Objects |
