Introduction to Python Programming
Take Your Next Step Towards Being A MDAE Certified Data Scientist
30 Hours | Live Interactive | Starts 13th Feb 2021 |
Weekend(Sat-Sun): 3:00 to 6:00 PM |
Avail Early Bird Offer | 15% Off Till 22nd of January |
7 Day Money Back Guarantee
Course Overview:
The course will introduce participants to the general principles of programming as also serve as a detailed introduction to the Python programming language. Participants will learn how to code in Python. They will also will they get introduced to certain higher concepts such as data handling and analysis in Python. It will serve as a first crucial step towards a career in data analysis and data science.
This course is an essential primer for anyone who wishes to pursue the Data Science Career track with Python.
- Those who are first-timers to programming will obtain a strong foundation to build a career in programming.
- Those who are already familiar with other programming languages will be able to quickly transition to programming in Python.
- Students will get comfortable with handling text corpuses.
- Students will be able to satisfy the Python programming pre-requisite for advanced Data Science courses involving Python coding.
- They will be equipped to commence work on achieving a Python coding rank with online coding websites such as Hackerrank.
Any individual who is:
- Interested in coding and learning to program in Python.
- Aspiring to be a data analyst or data scientist, with little or no formal training in the subject.
- The course does not require prior programming experience.
- A desire to think logically will be an asset.
- An exposure to Microsoft Excel as a computational tool would be an advantage.
- Basic understanding of logic and flowcharts would be an advantage.
- Students are encouraged to join the online course from their computer (rather than a mobile phone). It is advised that Python 3.x be installed on the student’s computer, so that the student can experiment with Python code after course hours.
Key Features
Live Coaching
30 hours instructor-led, live interactive training.
Continuous Assessment
3 assignments, 1 final project, pre & post course final exam.
Collaborative Building of Code Snippet
The instructor and students shall jointly build code snippets for stated problems.
Constant Learner Support
Digital office hours + teaching assistant + lifetime recording access on LMS.
Alumni Status
Industry event invitations + student network access
Career Enhancement
Support for Resume building & interview skills.
Meet Prof. Amlesh Kanekar Your Course Faculty
Amlesh Kanekar has 34 years of experience in Information Technology in the Banking & Finance domain. His first 8 years were in Tata Unisys Limited (now merged into Tata Consultancy Services), followed by the next 12 years as an international independent consultant on Unisys products and finally the last 14 years with Oracle Financial Services Software Limited (OFSS). His Leadership roles include Global Consulting Head for Digital Banking, Product development head for Digital Banking, Product QA head for Core Banking and Digital Banking, Delivery head for the virtual bank in Japan for Core Banking & Digital Banking.
Session-Wise Course Curriculum
- A logical view of your computer
- What does it mean to write a computer program?
- What are the basic things that a computer program does?
- Integrated Development Environments (IDEs)
- Commonly used Python IDEs
- The IDLE IDE
- A simple program in Python
- Printing to the screen
- Accepting input from the keyboard
- Saving the python program as a source code file
- The instructor will develop the code in class
- We will inspect the code and identify various components therein
- Variables
- Data types
- Constants
- Arithmetic expressions
- The assignment operator
- Python functions
- The three common ways to run your Python code
- Running code in the IDLE shell
- Running a source code file from IDLE
- Running a source code file from the command prompt
- Basic Python data types and variables
- Integer
- Float
- String
- Boolean
- Declaring variables of the above types
- The Python type function
- Return to the logical view of your computer’s memory to understand variable allocation
- Understanding the need for the NoneType in Python
- An in-depth look at the boolean type
- Python operators
- Arithmetic operators
- Relational operators
- Assignment operators
- Logical operators
- Identity operators
- Binary (bitwise) operators
- Python in-built functions – an introduction
- The Python print function
- The Python input statement
- sys.argv
- The string data type
- String indexing
- String functions
- Unicode strings
- Escape sequences in strings
- The if statement
- What is an if statement
- The philosophy behind an if statement
- Variations and nuances of the if statement
- if
- if…else
- if…elif…else
- Nested if’s
- A practical example demonstrating if statements and their usage
- Python in-built functions
- abs, float, int, len, str, max, min, print, input, range, round, sorted, sum, asci, chr, list, set, type, any, all, math.pow, math.sqrt, math.factorial, math.trunc, math.pi
- The Python list type
- Iterable types
- Subscriptable/indexable types
- Mutable type
- The list data type
- Creation of lists, list comprehension
- List type methods
- Programming Loops
- Why do we need to code loops
- For loops
- While loops
- Termination of loops, the break statement
- The continue statement
- Develop a Python program to implement a loop
- Functions
- Defining your own functions
- Function declaration
- Function name
- Function arguments
- Named and unnamed arguments
- Function body
- Return value(s)
- Organizing your code
- Placing your functions in distinct source code files (packages)
- Develop a Python program using a function we will create
- Defining your own functions
- The NumPy package
- Numpy arrays
- Numpy mathematical functions
- The pandas package
- Working with excel and csv files in Python
- Importing data from an excel sheet into Python and exporting data from Python to excel
- Assigning column names, extracting individual rows and columns
- Dealing with missing data
- Summarizing data
- An introduction to Linear Regression in Python
- Create a simple dataset with
- One normally distributed X variable
- One numeric Y variable which is a linear function of the X variable with a random error component
- Generate a linear regression model for the above dataset and learn to examine the output parameters of the regression
- Vary the extent of error in the Y variable and inspect its impact on the goodness of fit of the model
- Create a simple dataset with
Select Projects / Case Studies
Code Snippet 1
Candidates are being screened for the positions of security officers. Their ages must be between 25 and 35 years, both inclusive. Their weights must be between 65 and 85 kg. If the age is less than 30 years then the weight must be less than 75 kg. If the age is greater than or equal to 30 years then the weight must be greater than or equal to 75 kg.
Develop a Python code snippet to accept the appropriate inputs and report whether the candidate passes the screening test.
Develop test cases to test the above snippet.
Code Snippet 2
An analysis has been done about the Indian team's test match performance in England. The following observations are available. We want to draw reasonable conclusions about the outcome of a test match from them:
Before the test series begins, the Indian team always has a 3-day match with Surrey. The outcome of the first test match always behaves differently from the subsequent test matches. Observed characteristics of the first test match are as follows. Batting first, India loses if it had won the Surrey game. Batting second, India tends to lose if it's first innings total against Surrey was less than 300. If it was more than 300, the first test match tends to draw. Batting first, the test usually draws if India lost against Surrey.
Observed characteristics of the subsequent test matches are as follows. A test match (other than the first one) at the Oval is special. India wins if it plays more than two spinners, otherwise the test ends in a draw. At grounds other than the Oval, India loses if it plays three or less pacers; but if India fields more than 3 pace bowlers it is highly likely to win.
Develop a Python code snippet to accept the appropriate inputs for a specific test match and report the probable result of the test match.
Develop test cases to test the above snippet.
Code Snippet 3
Develop a Python code snippet that will receive a text corpus as an input and identify all palindromes in the corpus.
Alumni Track Record!
MDAE alumni working in diverse roles across leading companies.
Arushi Mishra
Data Consultant
Vallari Naik
Trainee Decision Scientist
Pooja Joshi
Senior Research Analyst
Nishitha Mehta
Risk Analyst
Swati Shrimali
Business Analyst
Ujas Shah
Research Analyst
What Our Students Say About Our Faculty
"Prof. Amlesh was an exceptional guiding force. Everything I have learned about Python through his course not only helped me for the exam, but I believe will help me a long way. He was patient with every student and every doubt. He made sure that everyone was comfortable with the pace he was going at. Apart from that, his course was also very well structured, Learning coding this way, in a step by step manner has really cleared my basics, which makes it easier for me not just to get comfortable with python, but every other programming language. The handouts provided were of great help as well, because during the lecture or even while practicing, there was a guide available to us on every step of the way. Overall, I believe that Prof. Amlesh's course was outstanding in every way and I look forward to learning more from him if the academy gives me the opportunity."
Sana Parikh
"Prof Amlesh is the most patient and eloquent professor ever. I have no background in data and programming, and he made everything seem so easy and understandable, that it never felt like I haven't done this before. He was always approachable, and always very open to helping students actually learn."
Neelesha Dhawan
₹46,500
(Excluding GST)
Career Track
(4 Courses | 120 Hours)
(MDAE Certified Data Scientist )
₹11,900
15 % Early-Bird Offer
₹10,115
(1 Course | 30 Hours)
(Introduction to Python)
MDAE’s learning journey offers a candidate multiple entry and exit points. For those with prior exposure to the field, they can choose one or more of the courses on offer as long as they meet the course prerequisites. For example – a candidate who understands the basics of programming, can straightway opt to study the wrangling course or even the machine learning course. On the other hand, the career track allows a participant to deep dive and go through a solid training right from the foundation to the advance level. The career track is a 120 hour, 4 course training program which allows the candidate to pick a programming language between R or Python. The first course (Quant essentials for data science) is common across the programming languages, and the next 3 courses are specific to what you choose (R or python)
There are 3 distinct advantages of choosing a career track over a skills track –
1) Different certification – With the integrated 4 course training, candidates will become an MDAE Certified Data Scientist – an industry demanded certificate.
2) Small group mentoring – Candidates will receive live mentoring with industry’s best practitioners throughout the program.
3) Capstone projects – Deserving candidates can be evaluated for projects/internships with our industry partners.
