<h2>Why Choose Data Science Training Program With Python In Delhi?</h2>

Python With Data Science Training And Course In Delhi

Take your IT career to new heights by becoming an expert with us

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Course
Duration

200

hours

Advantages of Python With Data Science Training Course

₹ 2,50,000+

Average Salary per Annum in India

$ 50,000

Average Salary per Annum in USA

15,00,000

Job Vacancies across the world

1,00,000

Python Developers in the world and the demand is increasing day by day

Why Choose Data Science Training Program With Python In Delhi?

Data Science is the process of deriving knowledge and insights from a huge and diverse set of data through organizing, processing and analyzing it. It involves many varied disciplines like mathematical and statistical modeling, extracting data from its source and applying data visualization techniques.The programming requirements of Data Science demand an exceptionally versatile yet flexible language which is simple enough to write the code but can also handle highly complex mathematical processing. Python is most aptly suited for such requirements as it has already build the reputation, both as a language for general computing as well as scientific computing. Thus, it will not be wrong to say that a professional who has completed their Data Science training program with Python in Delhi has ample amount of diversified opportunities in modern IT environment across the world.  

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Hiring Partners

We are globally connected with the top IT companies

We are connected with companies with different industries across the nation. Our dedicated placement cell is constantly striving to get more companies on-board

About Python With Data Science Programming Course

Today's modern IT world is driven by capturing, storing and analyzing data for various requirements. Data Science is everywhere and goes as far as making new strategy for business growth, forecasting weather, managing trends in every spectrum or designing a marketing campaign, financial risk management, etc. All of these scenarios involve a multidisciplinary approach of using mathematical models, statistics, graphs, databases, and business and scientific logic behind the data analysis.So we need a programming language which can cater to all these diversified need of data science. Python is one of the leading programming languages as it has numerous libraries and built in features which make it easy to tackle the need of data science. Going for a data science training program with Python in Delhi can be beneficial for aspiring individuals because of the increasing and expanding hold of IT sector in the city. 

Python With Data Science Programming

Tracks Regular Track Full Day(Fast Track)
Training Duration 200 hours 200 hours
Training Days 100 Days 25 Days
  • Introduction to Python, Basic Syntax, Data Types, Variables, 
  • Operators, Input/output, Flow of Control (Modules, Branching), If, If- else, 
  • Nested if-else, Looping, For, While, Nested loops, Control Structure, Break, Continue, Pass, 
  • Strings and Tuples, Accessing Strings, Basic Operations, String slices, Working with Lists, 
  • Introduction, Accessing list, assign and retrieve values from Lists, Introducing Tuples, Accessing tuples Operations, 
  • Function and Methods, Files, Modules, Dictionaries, Advance Dictionaries, Functions and Functional Programming, 
  • Declaring and calling Functions, Special Functions in python lambda, map and reduce. 
  • Advance functions in python var length arguments and Closures and Decorators.Namespace and Generator and Iterators.
  •  Object Oriented, OOPs concept, Class and object, 
  • Attributes, Inheritance, Overloading, Overriding, Data hiding, Meta Classes, 
  • Shared Memory concepts, Exception Handling, except clause, Try finally clause, 
  • User Defined Exceptions, Debugging modules pdb, doctest and loggers. 
  • Python Libraries- NUMPY, SCIPY, PANDAS, Scikit-Learn, matplotlib, bs4 etc. 
  • Probability & Statistics: Introduction to Statistics- Descriptive Statistics, 
  • Summary Statistics Basic probability theory, 
  • Statistical Concepts (uni-variate and bi-variate sampling, distributions, re-sampling, statistical Inference, prediction error), 
  • Probability Distribution (Continuous and discrete- Normal, Bernoulli, Binomial, Negative Binomial, Geometric and Poisson distribution) , 
  • Bayes’ Theorem, Central Limit theorem, Data Exploration & preparation, Concepts of Correlation, Regression, Covariance, Outliers etc. 
  • R Programming: Introduction & Installation of R, R Basics, Finding Help, Code 
  • Editors for R, Command Packages, Manipulating and Processing Data in R, 
  • Reading and Getting Data into R, Exporting Data from R, Data Objects-Data 
  • Types & Data Structure. Viewing Named Objects, Structure of Data Items, 
  • Manipulating and Processing Data in R (Creating, Accessing , Sorting data frames, Extracting, 
  • Combining, Merging, reshaping data frames), Control Structures, 
  • Functions in R (numeric, character, statistical), working with objects, Viewing Objects within Objects,
  •  Constructing Data Objects, Building R Packages, Running and Manipulating Packages, Non parametric Tests- ANOVA, 
  • chi-Square, t-Test, U-Test, Introduction to Graphical Analysis, Using Plots(Box 
  • Plots, Scatter plot, Pie Charts, Bar charts, Line Chart), 
  • Plotting variables, Designing Special Plots, Simple Liner Regression, Multiple Regression 
  • Database Concepts (File System and DBMS), Database Storage Structures 
  • (Tablespace, Control files, Data files), Structured and Unstructured data, SQL 
  • Commands (DDL, DML & DCL), Data ware Housing concept, No-SQL, Data Models - XML, working with MongoDB).
  • SQL Programming: Introduction to SQL, Download the dataset, Shortcut to upload the data, SELECT * Statement, 
  • Using the WHERE clause to filter data, 
  • How to use Wildcards / Regular Expressions in SQL (% and _), Comments in 
  • SQL, Order By, 
  • Data Types in SQL, implicit Data Conversion in SQL, Using 
  • Cast() vs Convert(), Working with NULLs, Understanding how LEFT, RIGHT, INNER, and OUTER joins work, Joins with duplicate values, Joining on multiple fields 
  • Tools - OLTP and OLAP, data preparation and cleaning techniques 
  • Basics of Linux – File Systems, Users, Groups, Permissions, Mounting, Shell 
  • Scripts, Ip & Networking, Firewall, Services and Daemons, Apache Server, MariaDB Server, CGI scripts 
  • Cloud Computing – Cloud Computing Concepts, Public and Private Cloud, Introduction to AWS, Azure, Digital Ocean and Google Cloud Services.  
  • Introduction to Big Data- Big data definition, enterprise / structured data, social/unstructured data, unstructured data needs for analytics, What is Big Data, Big Deal about Big Data, Big Data Sources, Industries using Big Data, Big Data challenges. 
  • Hadoop:- 
  • Introduction of Big data programming-Hadoop, History of Hadoop, 
  • The ecosystem and stack, The Hadoop Distributed File System (HDFS), Components of Hadoop, 
  • Design of HDFS, Java interfaces to HDFS, Architecture overview, Development Environment, 
  • Hadoop distribution and basic commands, Eclipse development, The HDFS command line and web interfaces, 
  • The HDFS Java API (lab), Analysing the Data with Hadoop, Scaling Out, 
  • Hadoop event stream processing, complex event processing, MapReduce Introduction, 
  • Developing a Map Reduce Application, How Map Reduce Works, The 
  • MapReduce Anatomy of a Map Reduce Job run, Failures, Job Scheduling, 
  • Shuffle and Sort, Task execution, Map Reduce Types and Formats, Map Reduce Features, Real-World MapReduce. 
  • Hadoop ETL:-
  • Hadoop ETL Development, ETL Process in Hadoop, Discussion of ETL functions, Data Extractions, Need of ETL tools, Advantages of ETL tools. 
  • Pig and HIVE- Programming Pig:-
  • Engine for executing data flows in parallel on Hadoop, Programming with Hive: Data warehouse system for Hadoop, 
  • Optimizing with Combiners and Practitioners (lab), More common algorithms: sorting, indexing and searching (lab), Relational manipulation: map-side and reduce-side joins (lab), 
  • evolution, purpose and use, HDFS – Overview and concepts, data flow (read and write), interface to HDFS (HTTP, CLI and Java API), high availability and Name Node federation, 
  • Map Reduce developing and deploying programs, optimization techniques, Map Reduce Anatomy, Data flow framework programming Map Reduce best practices and debugging,
  •  Introduction to Hadoop ecosystem, integration R with Hadoop.
  • Hadoop Environment:-
  • Setting up a Hadoop Cluster, Cluster specification, 
  • Cluster Setup and Installation, Hadoop Configuration, Security in Hadoop, Administering Hadoop, 
  • HDFS – Monitoring & Maintenance, Hadoop benchmarks, Hadoop in the cloud. 
  • Introduction to Apache Spark and Use Cases:-
  • Apache Spark APIs for large-scale data processing: Overview, Linking with 
  • Spark, Initializing Spark, Resilient Distributed Datasets (RDDs), External 
  • Datasets, RDD Operations, Passing Functions to Spark, Working with Key-Value 
  • Pairs, Shuffle operations, RDD Persistence, Removing Data, Shared Variables, Deploying to a Cluster 
  • Information Visualization, Data analytics Life Cycle, Analytic Processes and 
  • Tools, Analysis vs. Reporting, Modern Data Analytic Tools, Visualization Techniques, Visual Encodings, 
  • Visualization algorithms, Data collection and binding, Cognitive issues, Interactive visualization, 
  • Visualizing big data – structured vs unstructured, Visual Analytics, Geo-mapping, Dashboard Design  
  • Tableau: Introduction, Installing Tableau Desktop and Tableau Public (FREE), 
  • Challenge description + view data in file, Connecting Tableau to a Data file - CSV file, Navigating 
  • Tableau - Measures and Dimensions, Creating a calculated field, Adding colours, Adding labels and formatting, Exporting your worksheet.
  • Supervise Machine Learning:Introduction to machine learning, Linear Regression, Logistic Regression, 
  • Decision Trees, Support Vector Machine, and Recommender Systems.
  • Un-Supervise Machine Learning: Introduction to Un-Supervise machine Learning, 
  • Clustering, K-Means, and Principle Component Analysis (PCA).

Data Analyst :

- An analyst  who collects the data, processes it and performs statistical analysis. According to which he translates the figures into simpler language and helps the organisations and companies to understand how to make better business 

Software Developer / Engineer :

A software developer or en engineer who have the knowledge of Machine learning and can handle data science roles . People who are skilled in R, Python, Java or other relevant languages and having experts in knowledge graph mining or text mining.

Research Analyst:

A research analyst is a professional who prepares investigative reports on securities or assets for  a client use and is responsible for researching, analyzing, interpreting, and presenting data related to markets, operations, finance/accounting, economics, customers, and other information related to the field they work in.

Data Scientist:

Data scientists are those who crack complex data problems with their strong expertise in data science which includes python programming concept, data analysis and interpretation .Their work is to work with  various mathematics, statistics, computer science related problems and provide a  growth solution to the organisation.

Python Developer:

A developer who does the programming in python and  is responsible for writing server-side web application logics in Python . Python web developers usually develop back-end components , trying to connect the application with other web services, and support the front-end developers by the integration of their programming with the front end development 

  • Placement Assistance
  • Live Project Assessment
  • Lifetime Career Support
  • Lifetime Training Membership (Candidate can join same course again for purpose of revision and update at free of cost at our any center in India or you can solve your query by online help)
  • Python Based Exam Scenario Preparation Included IN Training

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Our Team

Mr Akhilesh Jain G

Akhilesh Jain

10+ Years Experience B.Tech, AWS-CSA, RHCE & RHCSS Expertise in Network Security & AWS

Mr. Rajat Goyal

Rajat Goyal

10+ Years Experience B.Tech, RHCE, RHCSS, Cloud Certified Expertise in Linux, Cloud & Scripting

Ravi Sarswat

10+ Years Experience MCA, RHCE, RHCVAExpertise in Linux & Virtualization

Mr . Gaurav Saluja

Gaurav Saluja

10+ Years Experience B.Tech, RHCA-Level5, RHCDS, RHCSS, RHCVA, RHCE Expertise in Linux Troubleshooting & Tuning

Sachin Yadav

Sachin Yadav

5+ Years Experience B.Tech, RHCE, Python Certified Expertise in R & Python ,Data science & Artificial Intelligence

Nidhi

Nidhi Sharma

4+ Years ExperienceMBA, Career CounselorExpertise in Career Counselling  

Mr. Pawan Khatri

Pawan Khatri

4+ Years Experience MCA, Career Counselor Expertise in Soft Skills Training

Nikhil Maheshwari

2.5 + years ExperienceB.Tech, RHCSA ,RHCEExpertise in Redhat Linux &Cloud computing(AWS)

Nidhi Singh Choudhary

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Jyoti Gautam

3 years experienceMCA Expertise in RHCSA, RHCE, AWS, Azure, GCP, Docker & container, K8, Openstack

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