Courses / Python Basics & Advanced / Djnago framework / Data Science

Data science & analytics, machine learning and Artificial intelligence; we all agree it's good but the confusion is what/how to study. We hear about multiple choices like data science with python programming, R, SAS, Bigdata etc. but your answer lies in these steps.
      Step 1: Python basics. We recommend it over R programming.
      Step 2: Learning data mining techniques. From csv files to databases like MySQL and from cloud based big data to web scraping first learn to mine your data.
      Step 3: Learning data analytics tools. Now that you have the data you want, let's sharpen our analytics tools like NumPy (ScipY), Pandas etc.
     Step 4: Learning machine learning algorithms. Data analytics tools used with supervised and unsupervised machine learning algorithms will give you correct predictions. We will learn many machine learning algorithms (in-depth), with a project on each.
     Step 5: Learning data vizualization. We are not done yet, the analytics report is complex and to understand the predictor and outcome relationship well, we will need to draw graphs using matplotlib and seaborn.

Duration & Pricing

  1. Duration: 4 months in Sat-Sun cycle with each session of 2 hours.
  2. 3 or 4 hours per class format available to reduce the class duration.
  3. Weekday and online classes available for python basics.

(1) Python Basics ₹ 7,000
(2,3,5) Data mining, analytics and visualization ₹ 15,000
Web scraping + MySQL + NumPy(Scipy) + Pandas + Matplotlib + Seaborn
(4) Machine learning algorithms and scikit-learn ₹ 22,000
Complete course: Python + Web scraping+ MySQL + NumPy(Scipy) + Pandas + Matplotlib + Seaborn + Machine learning + Scikit-learn ₹ 44,000

Upcoming Batches: Call +91-9986877711 to know the next available batch.
  1. Weekday & Online: On-demand
  2. Weekend batches: One new batch every month.
  3. Batch audiences are mostly working professionals between experience 4-15 years.
How it works? airline_seat_recline_extra Book Demo Class
  1. Step 1: Attend demo/meetup to understand the offerings.
  2. Step 2: Select the course syllabus and projects.
  3. Step 3: Join the course with payment or study loan.
Python Basics Topics - Sample notes -
  1. Introduction to python programming
  2. Variables, operators and application memory
  3. Control flow statements
  4. Primitive data types: Numbers and Strings
  5. List & List comprehensions
  6. Tuples, Dictionary and Sets
  7. Functions, lambda, comprehensions and higher order functions
  8. Introduction to GUI programming
Data mining, analytics & visualization
  1. Web scraping
    • urllib
    • beautiful soup
  1. Database connectivity
    • sqlite3
    • MySQL
  1. NumPy (SciPy)
  2. NumPy basics
    • Array creation
    • Basic operations
    • Universal functions
    • Indexing
    • Slicing
    • Iterating
  3. Shape manipulation
    • Changing array shape
    • Stacking arrays
    • Splitting array
  4. Copies and Views
    • No copy
    • Shallow copy
    • Deep copy
    • Functions and methods
  5. Broadcasting rules
  6. Indexing with array of indices
  7. Indexing with boolean array
  8. Indexing with strings
  9. Linear algebra operations
  10. Numpy benefits with matplotlib
  1. Pandas
    • Series and Dataframes
    • Creating dataframes from csv
    • Plotting csv data
    • Adding/deleting coulmns with index
    • Stack/Unstack/Transpose functions
    • Filtering & Sorting
    • Grouping
    • Ways to calculate outliers
    • Reading data from SQL databases
    • Exporting data to txt/csv/excel
    • Visualization with matplotlib
  1. Matplotlib & seaborn
    • Basics of graph plotting
    • Line plot
    • Scatter plot
    • Bar graph
    • Histogram
    • Contour plot
    • Pie chart
    • Grids
    • Text plot
    • Multi plot
    • 3D plotting
Machine learning algorithms - View sample notes -
  1. Supervised Learning
  2. Linear Regression
  3. Dimensionality reduction
  4. Logistic Regression
  5. KNN (k-Nearest Neighbour)
  6. SVM (Support vector machines)
  7. Random Forest
  8. Decision tree
  9. Naive Baes
  1. Unsupervised Learning
  2. K-means clustering
  3. Apriori algorithm for association
  1. Study loan
  2. Project assistance
  3. Internships and Placement assistance
  4. Certificate from top universities like MIT, Standford etc.
  1. About the trainer
    Best in industry trainers and guest lecturers from IISc and DRDO. You will get 3 classes before you make up your mind. So don't worry and see for yourself.
  2. Crash course
    Availability of crash courses are subjective to target audiences. Crash courses happen but they don't start everyday.
  3. About meetup
    We conduct a meetup of data scientists and enthusiasts every Tuesday evening. Feel free to come.
  4. Backup classes
    Backup class can be offered for upto 25% of total classes.
  5. Study loan
    It is provided by companies in collaboration with so don't worry about annoying paper works. Interest rates are also as low as 3.99%
  6. Internships and Placements
    Placement assistance is available only for working professionals with a minimum experience of 3 years.
  7. Certification
    You can write online tests on websites like coursera with exam fee payment. Our training is more than sufficient to pass the test with flying colors. also provides an in-house certificate.