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- New Announcement !!! Upcoming online batch of Data Science on Oct 10th 2020.
- +91 98106 00764
₹75,000.00 ₹39,500.00
Starting from Sep 26th, 2020
10:00 am – 1:00 pm
₹75,000.00 ₹39,500.00
You Save Rs. 35500/-
Data Science is an art of making data driven decisions. To make that data driven decision it uses scientific methods, processes, algorithms to extract knowledge and insights from data. It is used for examining, cleaning, manipulating, transforming and generating information from the data. Now a days in Business world data analytics plays a vital role to form decisions more scientifically and help to increase operational effieciency.
Top skill in demand now a days is to process raw data into business insights. There is no special programming language dedicated to data science but looking at the exciting features of the python language you can make your mind. Python has great features like fast and high computational capability, extremely compatible, cross platform support , distributed computing and vector arithmetic. In this course we will learn python programming, statistics and analytics used for business analytics. We will learn data wrangling, data cleansing as well as data visualization using popular Python libraries like Numpy, Pandas, Matplotlib, and seaborn. In this course you will get to learn apply exploratory data analytics the essential part of data analytics.
By the end of this you will be able to extract, read and write data from csv files, data cleansing, data manipulation, data visualization, run inferential statistics, understand the business problems, based on problems you will be able to select and apply machine learning models and deploy it.
Data Science is meant for all and everyone should go for this, learn to play with data and grasping required skills isn’t just valuable, its essential now. Does not matter from which field you – economics, computer science, chemical, electrical, are statistics, mathematics, operations you will have to learn this.
| LESSONS | LECTURES | DURATION |
|---|
| Probability | 1 Lecture | 20:00 |
| Random Variables | 1 Lecture | 25:00 |
| Probability Distribution | 1 Lecture | 21:00 |
| Central Limit Theorem | 1 Lecture | 25:00 |
| Sampling | 1 Lecture | 25:00 |
| Confidence Intervals | 1 Lecture | 25:00 |
| Hypothesis Testing | 1 Lecture | 25:00 |
| Chi Square Test | 1 Lecture | 25:00 |
| Anova Test | 1 Lecture | 25:00 |
Data Types | 1 Lecture | 50:00 |
| Basic statistics using data examples | 1 Lecture | 30:00 |
| Central tendencies | 1 Lecture | 43:00 |
| Correlation analysis | 1 Lecture | 34:00 |
| Data Summarization | 1 Lecture | 40:00 |
| Data Dictionary | 1 Lecture | 29:00 |
| Outliers /Missing Values | 1 Lecture | 30:00 |
| Basic Linear Algebra – dot product, matrix multiplication and transformations | 1 Lecture | 38:00 |
Overview | 1 Lecture | 12:00 |
| The Python Ecosystem | 1 Lecture | 15:00 |
| Why Python over R/SAS | 1 Lecture | 10:00 |
| What to expect after you learn Python | 1 Lecture | 35:00 |
Understanding and choosing between different Python versions | 1 Lecture | 34:00 |
| Setting up Python on any machine (Windows/Linux/Mac) | 1 Lecture | 24:00 |
Using Anaconda, the Python distribution | 1 Lecture | 20:00 |
Exploring the different third-party IDEs (PyCharm, Spyder, Jupyter, Sublime) | 1 Lecture | 30:00 |
Setting up a suitable Workspace | 1 Lecture | 8:00 |
Running the first Python program
| 1 Lecture | 23:00 |
Python Syntax | 1 Lecture | 15:00 |
Interactive Mode/ Script Mode Programming | 1 Lecture | 18:00 |
Identifiers and Keywords | 1 Lecture | 25:00 |
Single and Multi-line Comments | 1 Lecture | 28:00 |
Data Types in Python (Numbers, String, List, Tuple, Set, Dictionary) | 1 Lecture | 21:00 |
Implicit and Explicit Conversions | 1 Lecture | 22:00 |
Understanding Operators in Python | 1 Lecture | 26:00 |
Working with various Date and Time formats
| 1 Lecture | 28:00 |
Working with Numeric data types – int, long, float, complex
| 1 Lecture | 38:00 |
String Handling, Escape Characters, String Operations | 1 Lecture | 26:00 |
Working with Unicode Strings | 1 Lecture | 16:00 |
Local and Global Variables
| 1 Lecture | 12:00 |
Flow Control and Decision Making in Python | 1 Lecture | 15:00 |
Understanding if else conditional statements | 1 Lecture | 18:00 |
Nested Conditions | 1 Lecture | 25:00 |
Working in Iterations | 1 Lecture | 28:00 |
Understanding the for and while Loop | 1 Lecture | 21:00 |
Nested Loops | 1 Lecture | 22:00 |
Loop Control Statements– break, continue, pass | 1 Lecture | 26:00 |
Understanding Dictionary- The key value pairs | 1 Lecture | 28:00 |
List Comprehensions and Dictionary Comprehensions | 1 Lecture | 38:00 |
Functions, Arguments, Return Statements | 1 Lecture | 26:00 |
Packages, Libraries and Modules | 1 Lecture | 16:00 |
Error Handling in Python | 1 Lecture | 12:00 |
Reading data from files (TXT, CSV, Excel, JSON, KML etc.) | 1 Lecture | 15:00 |
Writing data to desired file format | 1 Lecture | 18:00 |
Creating Connections to Databases | 1 Lecture | 25:00 |
Working in Iterations | 1 Lecture | 28:00 |
Importing/Exporting data from/to NoSQL databases (MongoDB) | 1 Lecture | 21:00 |
Importing/Exporting data from/to RDBMS (PostgreSQL) | 1 Lecture | 22:00 |
Getting data from Websites | 1 Lecture | 26:00 |
Manipulating Configuration files | 1 Lecture | 28:00 |
Introduction to Data Wrangling Techniques | 1 Lecture | 15:00 |
Why is transformation so important | 1 Lecture | 18:00 |
Understanding Database architecture – (RDBMS, NoSQL Databases) | 1 Lecture | 25:00 |
Understanding the strength/limitations of each complex data containers | 1 Lecture | 28:00 |
Understanding Sorting, Filtering, Redundancy, Cardinality, Sampling, Aggregations | 1 Lecture | 21:00 |
Converting from one Data Type to another | 1 Lecture | 22:00 |
Introduction to Numpy and its superior capabilities | 1 Lecture | 15:00 |
Understanding differences between Lists and Arrays | 1 Lecture | 18:00 |
Understanding Vectors and Matrices, Dot Products and Matrix Products | 1 Lecture | 25:00 |
Universal Array Functions | 1 Lecture | 28:00 |
Understanding Pandas and its architecture | 1 Lecture | 21:00 |
Getting to know Series and DataFrames, Columns and Indexes | 1 Lecture | 22:00 |
Getting Summary Statistics of the Data | 1 Lecture | 26:00 |
Data Alignment, Ranking & Sorting | 1 Lecture | 28:00 |
Combining/Splitting DataFrames, Reshaping, Grouping | 1 Lecture | 38:00 |
Identifying Outliers and performing Binning tasks | 1 Lecture | 26:00 |
Cross Tabulation, Permutations, the apply() function | 1 Lecture | 16:00 |
Introduction to Data Visualization | 1 Lecture | 12:00 |
Line Chart, Scatterplots, Box Plots, Violin Plots | 1 Lecture | 12:00 |
What is machine learning | 1 Lecture | 15:00 |
Different stages of ML project | 1 Lecture | 18:00 |
Supervised vs Unsupervised ML | 1 Lecture | 25:00 |
Algorithms in Supervised and Unsupervised learning | 1 Lecture | 28:00 |
Introduction to Sklearn | 1 Lecture | 21:00 |
Data preprocessing | 1 Lecture | 22:00 |
Scaling techniques | 1 Lecture | 26:00 |
Training /testing / validation datasets | 1 Lecture | 28:00 |
Feature Engineering | 1 Lecture | 38:00 |
How to deal with Categorical Variables – Dummy variables | 1 Lecture | 26:00 |
Categorical embedding | 1 Lecture | 16:00 |
Detailed explanation of Linear Regression – Linear regression assumption | 1 Lecture | 15:00 |
Cost function | 1 Lecture | 18:00 |
Gradient Descent | 1 Lecture | 25:00 |
Linear regression using sklearn | 1 Lecture | 28:00 |
Model accuracy metrics – RMSE , MSE, MAE | 1 Lecture | 21:00 |
R2 vs Adjusted R2 | 1 Lecture | 22:00 |
Detailed explanation of Logistics Regression | 1 Lecture | 15:00 |
Cost function | 1 Lecture | 18:00 |
Logistics equation | 1 Lecture | 25:00 |
Model accuracy metrics – Accuracy, ROC, Confusion Matrix, AUC | 1 Lecture | 28:00 |
What are decision trees? | 1 Lecture | 15:00 |
CART algorithms | 1 Lecture | 18:00 |
Shortcoming of decision trees | 1 Lecture | 25:00 |
Bagging and Boosting | 1 Lecture | 28:00 |
Random Forest | 1 Lecture | 21:00 |
Gradient Boosting | 1 Lecture | 22:00 |
Explanations using sklearn | 1 Lecture | 26:00 |
XGBoost | 1 Lecture | 28:00 |
k Means Clustering | 1 Lecture | 15:00 |
DBSCAN Clustering | 1 Lecture | 18:00 |
PCA | 1 Lecture | 25:00 |
Support Vector Machines | 1 Lecture | 28:00 |
Naive Bayes Classifier | 1 Lecture | 21:00 |
Feature selection techniques | 1 Lecture | 22:00 |
Overfit vs Underfit | 1 Lecture | 15:00 |
Bias Variance tradeoff | 1 Lecture | 18:00 |
Grid Search | 1 Lecture | 25:00 |
Random Search | 1 Lecture | 28:00 |
Feature Engg examples | 1 Lecture | 21:00 |
Ridge / Lasso Regression | 1 Lecture | 22:00 |
SkLearn Pipelines | 1 Lecture | 26:00 |
SkLearn Imputers | 1 Lecture | 28:00 |
| TOTAL | 28 LECTURES | 84:20:00 |
|---|
Hi I am Kushal and I am super excited that you are reading this.
Professionally, I am a data science management consultant with over 8+ years of experience in Banking, Capital Market, CCT, Media and other industry. I was trained by best analytics mentor at dunnhumby and now a days I leverage Data Science to drive business strategy, revamp customer experience and revolutionize existing operational processes.
From this course you will get to know how I combine my working knowledge, experience and qualification background in computer science to deliver training step by step.
Kushal is a good instructor for Data science. He cover all real world projects. He provided very good study materials and high support provided by him for interview prepration.
This is a very good place to jump start on your focus area. I wanted to learn python with a focus on data science. Kushal who is the faculty, is an accomplished and learned professional. He is a very good trainer. This is a very rare combination to find.
Thank you Deepak…
Good environment and nice place to learn data science. Trainer and staff is really supportive and they help me every time if I have any issue regarding my assignments and they also help me in job placement also.
Thanks shashi thank you very much
Add Reviews about your experience with us.
Data Science is an art of making data driven decisions. To make that data driven decision it uses scientific methods, processes, algorithms to extract knowledge and insights from data. Data science is related to data mining, Data Wrangling, machine learning and data visualization.
Data Science includes different processes like data gathering, data wrangling, data preprocessing, statistics, data visualization, machine learning. The mandate steps are Data preprocessing -> Data Visualization -> Exploratory Data Analysis ->Machine Learning -> Predictive Analysis
Pool of working professionals with several years experience in same field with different domains like banking, healthcare, retail, ecommerce and many more.
Along with the high quality training you will get a chance to work on real time projects as well, with a proven record of high placement support.
We will cover SQL for data gathering, Python programming, Machine Learning, Data Visualization with tableau.
Its Live interactive training, Ask your quesries on the go, no need to wait for doubt clearing.
you will have access to all the recordings, you can go through the recording as many times as you want.
Ask your questions on the go or you can post your question in group on facebook, our dedicated team will answer every query arises.
Yes we will help learners even after the subscription expires.
No you cannot download the recording it will be in your user access on LMS, you can go through at any point of time.
During the training and after as well we will be on the same slack channel, where trainer and admin team will share study material, data, project, assignment.
you can write your questions at info@palin.co.in we will address your questions there.
Palin is devoted to making continuous opportunities easily attainable, challenging as well as relevant to learners. Its aim is to facilitate Industry relevant as well as corporate suite training through exceptional management of operations and resources.
Lets us know your experience, it will help others.