Data Science Online Training

Data Science Online Training

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Course Overview

Data Science with Python is widely used for machine learning and artificial intelligence. The employment opportunities are unlimited in this field and will remain the same shortly. If we consider the salary packages for this, the average salary for Data Scientists is overboard. That demand is more than supply.

So it is just like a pie falling from the sky if you’re an expert in Data Science with Python or others.

What is Data Science?

In today’s technological era, everything is related to data and its storage. There is an unlimited amount of data collected. Most electronic devices have limitations due to the storage of data. Solutions related to storage of data is to develop smarter storage spaces that can store important data.

As the name says, Data Science is a study of data. Like a Physics experiment, you get to analyze, observe, manipulate, and process data to get a clear understanding of the data set. Data Science can be a versatile field that extracts insights via scientific interference and mathematics algorithms from a lot of unstructured and structured data. Like a lot of processing to be done on structured and unstructured data collected, we need algorithms that are nothing but software programs that require hardware to run the program. 

Data Science

Data Science is actually the combo of statistical mathematics, data analysis, machine learning, and one should have visualization. The name Data Science clearly tells us that Data Science is all about data. You cannot get any correct insights from inappropriate information, even if you use the most advanced algorithm. Data for image, video, text, and the data that is time dependant all are included in the insights that are sorted in Data Science Data science is a kind of investigation to unfold the truth, hiding behind data. When the truth behind data comes forward, you’re able to see a complete vision related to that data. This vision is of great importance and can be used for tactical planning for organizations or companies.

The study of data is done in data science. This study of data involves the analysis of data change and data behaviour. Data science is actually a combo of statistics, data analysis, and methods connected to find the truth behind data. Regarding mathematics, statistics, computer science, domain knowledge, information science, field techniques, and theories are drawn. Data Science is a collaboration of various algorithms, scientific methods, algorithms, and systems to extricate data, which is structured or unstructured data and knowledge. Big data, data mining, machine learning are all connected to data science. The world the sci-fi movies create is possible now to make it a reality with the help of Data Science. Data Science will be a pioneer in developing things that we see in Hollywood movies.

In the future, we can also develop flying cars just like we see in the movies, and again, we may create an army of robots working just like humans, as we see in movies. Just like IoT, there will be many more concepts or technologies will develop that will take us to advancement, making our future easy. All this will be possible because of Data Science. Data Science is widely known and gaining a lot of fame due to its demands for market jobs. The job opportunities offered to come with high salary packages, and many well-known companies like Amazon, Uber, and Apple demand professionals from this field.

History of Data Usage

In 1962 it was known as Data Analysis by John Tuckey. By establishing concepts and principles related to data analysis and data statistics, a new discipline around data of various fields and forms was announced in 1992 at the University of Montpellier. Around 1980 and 1990, this concept came into existence, as per some IT professionals, professors, and some scientists researching statistics by looking at the data, they gave the name Data Science. 

Manipulating data, whether it is proper or improper data, by analyzing and trying to get the insight from data, is nothing but Data Science. Around 2001, William S Cleveland invoked the concept of Data Science. International Council for Science: Committee on Data for Science publication “CODATA Data Science Journal” and Columbia University’s “Journal for Data Science” published in April 2002 and January 2003 actually started the journey of Data Science.

Data Science recently became very popular, Data Science came into existence due to the need for storage space for data that is being collected, and it is impossible to store large amounts of data, Data Science because of its cheaper computation algorithms which can process large amounts of data and get appropriate data insight.

Previously we did not have algorithms, but now we have to process large numbers of data for available data that can be processed. 


1. Fundamentals of Data Science and Machine Learning
  • Introduction to Data Science
  • The need for Data Science
  • BigData and Data Science’
  • Data Science and machine learning 
  • Data Science Life Cycle
  • Data Science Platform
  • Data Science Use Cases 
  • Skill Required for Data Science
2. Mathematics For Data Science
  • Linear Algebra
    • Vectors
    • Matrices
  • Optimization
    • Theory Of optimization
    • Gradients Descent
3. Introduction to Statistics
  • Descriptive vs. Inferential Statistics
  • Types of data
  • Measures of central tendency and dispersion
  • Hypothesis & Inferences
  • Hypothesis Testing
  • Confidence Interval
  • Central Limit Theorem
4. Probability and Probability Distributions
  • Probability Theory
  • Conditional Probability
  • Data Distribution
  • Binomial Distribution
  • Normal Distribution
1. An Introduction to Python
  • Why Python, its Unique Feature and where to use it?
  • Python Environment Setup/shell
  • Installing Anaconda
  • Understanding the Jupyter notebook
  • Python Identifiers, Keywords
  • Discussion about installed modules and packages
2. Conditional Statement, Loops, and File Handling
  • Python Data Types and Variable
  • Condition and Loops in Python
  • Decorators
  • Python Modules & Packages
  • Python Files and Directories manipulations
  • Use various files and directory functions for OS operations
3. Python Core Objects and Functions
  • Built-in modules (Library Functions)
  • Numeric and Math’s Module 
  • String/List/Dictionaries/Tuple
  • Complex Data structures in Python
  • Python built-in function
  • Python user-defined functions
4. Introduction to NumPy
  • Array Operations
  • Arrays
  • Functions
  • Array Mathematics
  • Array Manipulation
  • Array I/O
  • Importing Files with Numpy
5. Data Manipulation with Pandas
  • Data Frames
  • I/O
  • Selection in DFs
  • Retrieving in DFs
  • Applying Functions
  • Reshaping the DFs – Pivot
  • Combining DFs
    • Merge
    • Join
  • Data Alignment
6. SciPy
  • Matrices Operations
  • Create matrices
    • Inverse, Transpose, Trace, Norms , Rank etc
  • Matrices Decomposition
    • Eigen Values & vectors
    • SVDs
7.  MatPlotLib
  • Basics of Plotting
  • Plots Generation
  • Customization
  • Store Plots
8. SciKit LearnBasics
  • Data Loading
  • Train/Test Data generation
  • Preprocessing
  • Generate Model
  • Evaluate Models
1. Exploratory Data Analysis
  • Data Exploration
  • Missing Value handling
  • Outliers Handling
  • Feature Engineering
2. Feature Selection
  • Importance of Feature Selection in Machine Learning
  • Filter Methods
  • Wrapper Methods
  • Embedded Methods
3. Machine Learning: Supervised Algorithms Classification
  • Introduction to Machine Learning
  • Logistic Regression
  • Naïve Bays Algorithm
  • K-Nearest Neighbor Algorithm
  • Decision Tress (SingleTree)
  • Support Vector Machines
  • Model Ensemble
    • Bagging
    • Random Forest
    • Boosting
    • Gradient Boosted Trees
  • Model Evaluation and performance
    • K-Fold Cross Validation
    • ROC, AUC etc…
4. Machine Learning: Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Decision Tree and Random Forest Regression
5. Machine Learning: Unsupervised Learning Algorithms
  • Similarity Measures
  • Cluster Analysis and Similarity Measures
  • K-chical Clustering
  • Principal means Clustering
  • HierarComponents Analysis
  • Association Rules Mining & Market Basket Analysis
6. Text Mining
  • Basics
  • Term Document Matrix
  • TF-IDF
  • Twitter Sentiment Analysis

Why choose Python Training in Pune

Many will go for this training with just looking at the job opportunities that come with a high salary package. Still, the most important factor is you don’t need any prerequisites for this training. You can take the Data Science Online Course with or without any prior knowledge related to this. The premier platform for data science competitions and you can learn and show your skillset. With the right training and guidance, you can be a certified Data Scientist with a Data Science course. You can improve your skillset and can become the assets for the companies.

Our trainers are professionals in Data Science Course and are well-groomed in providing training. Even live training seems to like classroom training. We deliver planned and based sessions on a theory that goes along with a hands-on-based approach. We provide training with a practical approach, and every student is given individual attention.

We undertake live projects from companies to provide the students with hands-on experience on live projects. With these projects, they come to know about their weakness. These projects help them to learn them a lot.

Even if any students missed any session, the trainers would guide them by taking one on one session and make sure all the students are at the same pace in the batch.

We conduct online exams so that the trainer and student will know the progress of the training. Trainers help the students through their difficulties. Online exams are also for the certification process. So the course completion validates that students are proficient in Data Science.

Python Training Institute is one of the team of experts
from Industry Professional, who are working in Advanced
Technologies since more than 12 years.

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