Institute for Data Science Training Course Delhi, Gurgaon and Noida

Data Science Certification Course with 100% Job in Gurugram

Data Analytics Training in Gurgaon

Python Analytics

Tableau Dashboard & Analytics

SAS Analytics

MS Power BI Dashboard & Analytics

Data Science Course Gurgaon

What is the correct answer for 22 + 14

Data Science Training Course in Gurgaon

Data Science Training

Industry acceptable Data Science Training for all learner’s training which help fresher/Experienced to up-skill at corporate.

Experienced Faculty

Industry Expert Sr. Lead Analyst / Technical Analyst With 10+ Years provide workshop session @ SLA

Placement Assistance

After completion of 70% of Data Science training our dedicated placement team arrange interview till placement.

Lab Facility

Data Science Training help to gain exposure like corporate level with technical test series

Data Science Workshop Sessions

Real time projects and best case study makes SLA workshop very unique and lively for learners.

Admin Support

For Learner’s, Our admin team fresh batch schedule/re-scheduling classes/arrange doubt classes.

Data Science refers to a quantitative and qualitative method and process which is used to increase the productivity and business profitability. It is a technique of extracting, acknowledging and analyzing information such as behavioral data, business patterns, and techniques which are dynamic and necessary for business. Every business organization needs to perform Data Science which can provide various benefits such as increased customer satisfaction, enhancing the productivity and performance of the organization and can also provide the companies with the biggest growth opportunities. Data science is also considered an internal function of any business organization which deals with numbers and figures. Intercourse deep knowledge of recording and analyzing along with dissecting information and presenting the findings to make better decisions making for the management. However, in order to become a Data Science professional, one should also have knowledge in various Data Science tools such as Python, Power BI R-Programming, MS Excel and Access, Visual Basic for Application and Macros(VBA/Macros), SQL, Tableau and Business Intelligence tools. The best way to gain relevant skills and masteries Business Intelligence and Data Science tools is by attending quality Data Science Training in Delhi NCR, Noida & Gurgaon provided by SLA Consultants India. The Data Science Training Program is designed by industry experts that provides extensive and comprehensive expertise in different Data Science techniques and tools, allowing the participant to become an expert quickly. The Data Science with Python & R-Programming Training Program will help the learners acquire expertise in predicting customer Trends and behaviour, analysing, interpreting and delivering information in meaningful ways, driving effective decision making along with enhancing the business productivity. Anyone with a graduation degree is eligible to attend High-quality Data Science Training Course in Delhi NCR which is specifically targeted towards both freshers and working professionals who want to enter into the field of Data Science or become fluent in Data science techniques. The Data Science Certification Training is conducted by highly certified subject matter experts with the word 10 to 15 years of experience in the relevant field.

The Data Science Training Course in Delhi NCR is a dedicated and intelligently designed Data Science Training Course that is targeted towards an individual who is good with the numbers and figures and wants to lead a successful career in the Data Science field. The entire Data Analyst Training is divided into five different models which can be completed within 150 hours. The major highlight of the Data Science Training Course is the Data Science course content which is highly updated as per the industrial standards. It covers a wide range of advanced topics in Data Science which include Excel and VBA macros Analytics, SQL and MS Access, Python Data Visualization, Tableau and MS Power Business Intelligence(BI), R-Programming & Python. Practical Training in Data Science will be provided to the learner to gain experience in Business Data Science and acquiring technical expertise in data models, segmentation techniques, data mining techniques, database design development, etc. It will also help them to develop strong analytical skills allowing them to easily collect, organize and analyze a large amount of information with accuracy and efficiency. Upon completion of the Data Science Training Course in Delhi NCR, Noida & Gurgaon, the participants will be able to interpret Data and Analyse results using Statistical Techniques and provide reports, develop and implement databases, Data Science and data collection system along with other strategies to increase the efficiency and quality, gathering information from primary and secondary data sources, identifying and analysing latest trends and patterns, filtering the information by reviewing the computer reports and performance indicators to identify any issues and much more.

The participants will also be provided with in depth understanding of Data Science using live projects and assignments on Real world cases so that they would not require additional Data Science Training after getting placement. Proper workshops and handouts will be conducted by the teachers to offer them industry relevant knowledge and resolve any of their issues. There are various other job profiles which an individual can pursue after completing the Data Science Training Course in Delhi NCR, Noida & Gurgaon which include Data Analyst, Senior Analyst, Data Manager, Data Scientist, Business Intelligence professional, etc. Upon completion of the Data Science training, valid certification in Data Science will be awarded to all the learners to help them gain the competitive edge over other candidates during the interview. 100% Job Placement Assistance will also be provided to the candidates who successfully cleared the Data Science Training Course by providing them resume building services along with mock interviews to make them ready for entering into the same market.

Over the last few years, Data Science has become one of the most important business functions worldwide due to technical Advancement which has resulted in increased generation of information on a regular basis. Every business organization regardless of their scale and size requires data in order to identify the behaviour and trends in the Business Industry which can be used to make better decision making and satisfy the needs of the customers. This has also increased the requirement for professional and knowledgeable Data Analysts in the market. There are various other reasons why one should learn Data Science and enter into the field. Some of these reasons are mentioned below:

With the continuous generation of information which is expected to increase 50 times by 2020, there is an immense shortage of talent of skilled candidates. Professionals who are able to analyse the information will have increased requirement in the future.
According to Gartner, there will be three hundred percent increase in the jobs related to Data Science by the year 2018. This is another reason to Learn Data Science as it will offer the individual better job security.
Data Science is one field which is not restricted by one industry. Therefore, after acquiring skills and expertise in Data Analysis, you can land a job at various Business Industries such as E-commerce, Real-Estate, telecommunication, healthcare, retail and much more.
One of the best reason to learn Data Science is the kind of payscale received by the professional. Starting salary for Data Science for freshers can be 3 lakh per annum as of 2018 which can go up to 10 lakh per annum as per the experience.
There is also great growth opportunities in the Data Science field as you will be working with various other professionals. You will get to learn new things which will be beneficial for your career in the future.

Therefore, if you have made up your mind to learn Data Science, then you can visit our Data Science Training institute in Delhi, Gurgaon or Noida for registration or submit your quotation on our official website.

Data Science Training Practical with Interview Guarantee

Our Trainer:- 10+ Years of industry experience as a Data Analyst in MNC, Education:- B.Tech

Course Duration: 150 (Hours) with Highly Skilled Corporate Trainers

(Data Science Training) for 5 Modules – Weekdays / Weekend

SLA Consultants India – Data Science Training Delivery Method:

Web / Desktop Base.
Instructor-Led Classroom Sessions.
Presentations/Demonstration/Practicals of concepts.
Handouts/Assignments/Real-time Exposure to work on Clients.

Data Science Training Programme Modules

Data Science Training Course

Module 1 – Python Overview

Module 2 – Machine Learning by Using R-Programming

FUNDAMENTAL OF STATISTICS

Population and sample
Descriptive and Inferential Statistics
Statistical data analysis
Variables
Sample and Population Distributions
Interquartile range
Central Tendency
Normal Distribution
Skewness.
Boxplot
Five Number Summary
Standard deviation
Standard Error
Emperical Formula
Central limit theorem
Estimation
Confidence interval
Hypothesis testing
P-value
Scatterplot and correlation coefficient
Standard Error
Scales of Measurements and Data Types
Data Summarization
Visual Summarization
Numerical Summarization
Outliers & Summary

Module 2.1- Introduction to Data Analytics

Objectives:

This module introduces you to some of the important keywords in R like Business

Intelligence, Business
Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems.
This module tells you what is R and how it is used by the giants like Google, Facebook, etc.
Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other Software in analytics, install R and its packages.

Topics:

Business Analytics, Data, Information
Understanding Business Analytics and R
Compare R with other software in analytics
Install R

Module 2.2- Introduction to R programming

Starting and quitting R

Recording your work

Basic features of R.
Calculating with R
Named storage
Functions
R is case-sensitive
Listing the objects in the workspace
Vectors
Extracting elements from vectors
Vector arithmetic
Simple patterned vectors
Missing values and other special values
Character vectors Factors
More on extracting elements from vectors
Matrices and arrays
Data frames
Dates and times

NOTE:-

Assignments with Datasets

Import and Export data in R

Importing data in to R
CSV File
Excel File
Import data from text table

Topics

Variables in R
Scalars
Vectors
R Matrices
List
R – Data Frames
Using c, Cbind, Rbind, attach and detach functions in R
R – Factors
R – CSV Files
R – Excel File

NOTE-:

Assignments
Business Scenerio/Group Discussion.

R Nuts and Bolts-:

Entering Input. – Evaluation- R Objects- Numbers- Attributes- Creating Vectors- Mixing Objects-Explicit Coercion- Summary- Names- Data Frames.

Module 2.3- Managing Data Frames with the dplyr package

The dplyr Package
Installing the dplyr package
select()
filter()
arrange()
rename()
mutate()
group_by()
%>%

NOTE-:

Assignments
Business Scenerio/Group Discussion.

Module 2.5 – Data Manipulation in R Objectives:

In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
Thus using and exploring the popular functions required to clean data in R.

Topics

Data sorting
Find and remove duplicates record
Cleaning data
Merging data

Statistical Plotting-:

Bar charts and dot charts
Pie charts
Histograms
Box plots
Scatterplots
QQ plots

NOTE:-

Assignments with Datasets

Objectives:

Control Structure Programming with R
The for() loop
The if() statement
The while() loop
The repeat loop, and the break and next statements
Apply
Sapply
Lapply

NOTE:

Assignments with Datasets

Factors

Using Factors
Manipulating Factors
Numeric Factors
Creating Factors from Continuous Variables
Convert the variables in factors or in others.

Reshaping

Data Modifying
Data Frame Variables
Recoding Variables
The recode Function
Reshaping Data Frames
The reshape Package

NOTE:-

Assignments with Datasets

Explore many algorithms and models:

Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine!

Module 2.6- Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning

Machine Learning Languages, Types, and Examples
Machine Learning vs Statistical Modelling
Supervised vs Unsupervised Learning
Supervised Learning Classification
Unsupervised Learning

Module 2.7 – Supervised Learning I

K-Nearest Neighbors
Decision Trees
Random Forests
Reliability of Random Forests
Advantages & Disadvantages of Decision Trees

Module 2.8 – Supervised Learning II

Regression Algorithms
Model Evaluation
Model Evaluation: Overfitting&Underfitting
Understanding Different Evaluation Models

Module 2.9 – Unsupervised Learning

K-Means Clustering plus Advantages & Disadvantages
Hierarchical Clustering plus Advantages & Disadvantages
Measuring the Distances Between Clusters – Single Linkage Clustering
Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
Density-Based Clustering

Module 2.10 – Dimensionality Reduction & Collaborative Filtering

Dimensionality Reduction: Feature Extraction & Selection
Collaborative Filtering & Its Challenges

Module 2.11 – Classification

An Overview of Classification.
Why Not Linear Regression?
Logistic Regression
The Logistic Model
Estimating the Regression Coefficients
Making Predictions
Logistic Regression for 2 Response Classes
Lab: Logistic Regression.
The Stock Market Data
Logistic Regression

NOTE

Assignments with Different Datasets
Business Scenerio/Group Discussion

Module 2.12 – Tree-Based Methods

The Basics of Decision Trees
Regression Trees
Classification Trees
Trees Versus Linear Models
Advantages and Disadvantages of Trees
Bagging, Random Forests, Boosting
Bagging
Random Forests
Lab: Decision Trees
Fitting Classification Trees
Fitting Regression Trees

NOTE-:

Assignments with Different Datasets.
Business Scenerio/Group Discussion.

Module 2.13 – Time Series &Forecasting:

Time series
Estimating and Eliminating the Deterministic Components if they are present in the Model.
Estimating and Eliminating Seasonality if it is present in the Model
Modeling the Remainder using Auto Regressive Moving Average (ARMA) Models
Identify ‘order’ of the ARMA model
Forecast’ or Predict for Future Values
Practise on R

Module 2.14- Support Vector Machines – Outline

Understand when the Support Vector family of methods are an appropriate method of analysis.
Understand what a hyperplane is and how they are used with the Support Vector methods.
Identify the differences between Maximal Margin Classifiers, Support Vector Classifiers, and Support
Vector Machines.
Know how each of the algorithms determines the best separating hyperplane.
Distinguish between hard and soft margins and when each is to be used.
Know how to extend the method for nonlinear cases.

Module 2.15 – Principal Component Analysis

Understand what principal components are and when principal component analysis is appropriate.
Describe eigenvalues and eigenvectors and how they are used to calculate principal components.
Understand loading and loading vectors.
Know how to decide how many principal components to use in the analysis.
Be able to use principal component analysis for regression.

Module 3 – Python Machine & Deep Learning Course

Introduction to Python

Introduction to Programming
Why Python?
Why Jupyter?
Installing Python and Jupyter
Understanding Jupyter’s Interface – the Notebook Dashboard
Prerequisites for Coding in the Jupyter Notebooks
Jupyter’s Interface
Python 2 vs Python 3

Python – Variables and Data Types

Variables
Numbers and Boolean Values in Python
Python Strings

Python – Basic Python Syntax

Using Arithmetic Operators in Python
The Double Equality Sign
How to Reassign Values
Add Comments
Understanding Line Continuation
Indexing Elements
Structuring with Indentation

Python – Other Python Operators

Comparison Operators
Logical and Identity Operators

Python – Conditional Statements

The IF Statement
The ELSE Statement
A Note on Boolean Values

Python Functions

Defining a Function in Python
How to Create a Function with a Parameter
Defining a Function in Python – Part II
How to Use a Function within a Function
Conditional Statements and Functions
Functions Containing a Few Arguments
Built-in Functions in Python
Python Functions

Python – Sequences

Lists
Using Methods
List Slicing
Tuples
Dictionaries

Python – Iterations

For Loops
While Loops and Incrementing
Lists with the range() Function
Conditional Statements and Loops
Conditional Statements, Functions, and Loops
How to Iterate over Dictionaries

Python – Advanced Python Tools

Object Oriented Programming
Modules and Packages
What is the Standard Library?
Importing Modules in Python

Advanced Statistical Methods in Python

Introduction to Regression Analysis

Advanced Statistical Methods – Linear regression

The Linear Regression Model
Correlation vs Regression
Geometrical Representation of the Linear Regression Model
Python Packages Installation
First Regression in Python
Using Seaborn for Graphs
How to Interpret the Regression Table
Decomposition of Variability
What is the OLS?
R-Squared

Advanced Statistical Methods – Multiple Linear Regression

Multiple Linear Regression
Adjusted R-Squared
Multiple Linear Regression Exercise
Test for Significance of the Model (F-Test)
OLS Assumptions
A1: Linearity
A2: No Endogeneity
A3: Normality and Homoscedasticity
A4: No Autocorrelation
A5: No Multicollinearity
Dealing with Categorical Data – Dummy Variables
Making Predictions with the Linear Regression

Advanced Statistical Methods – Logistic Regression

Introduction to Logistic Regression
A Simple Example in Python
Logistic vsLogit Function
Building a Logistic Regression
An Invaluable Coding Tip
Understanding Logistic Regression Tables
What do the Odds Actually Mean
Binary Predictors in a Logistic Regression
Calculating the Accuracy of the Model
Underfitting and Overfitting
Testing the Model

Advanced Statistical Methods – Cluster Analysis

Introduction to Cluster Analysis
Some Examples of Clusters
Difference between Classification and Clustering
Math Prerequisites

Advanced Statistical Methods – K- Means Clustering

K-Means Clustering
A Simple Example of Clustering
Clustering Categorical Data
How to Choose the Number of Clusters
Pros and Cons of K-Means Clustering
To Standardize or not to Standardize
Relationship between Clustering and Regression

Advanced Statistical Methods – Other Types of Clustering

Types of Clustering
Dendrogram&Heatmaps

Mathematics

What is a matrix?
Scalars and Vectors
Linear Algebra and Geometry
Arrays in Python – A Convenient Way To Represent Matrices
What is a Tensor?
Addition and Subtraction of Matrices
Errors when Adding Matrices
Transpose of a Matrix
Dot Product
Dot Product of Matrices
Why is Linear Algebra Useful?

Deep Learning

What to Expect from this Part?
What is Machine Learning

Deep Learning – Introduction to Neural Networks

Introduction to Neural Networks
Training the Model
Types of Machine Learning
The Linear Model (Linear Algebraic Version)
The Linear Model
The Linear Model with Multiple Inputs
The Linear model with Multiple Inputs and Multiple Outputs
Graphical Representation of Simple Neural Networks
What is the Objective Function?
Common Objective Functions: L2-norm Loss
Common Objective Functions: Cross-Entropy Loss
Optimization Algorithm: 1-Parameter Gradient Descent
Optimization Algorithm: n-Parameter Gradient Descent

Deep Learning – How to Build a Neural Network from Scratch with NumPy

Deep Learning – TensorFlow: Introduction

How to Install TensorFlow
A Note on Installing Packages in Anaconda
TensorFlow Outline and Logic
Actual Introduction to TensorFlow
Types of File Formats, supporting Tensors

Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks

What is a Layer?
What is a Deep Net?
Digging into a Deep Net
Non-Linearities and their Purpose
Activation Functions
Activation Functions: Softmax Activation
Backpropagation
Backpropagation picture
Backpropagation – A Peek into the Mathematics of Optimization

Deep Learning – Overfitting

What is Overfitting?
Underfitting and Overfitting for Classification
What is Validation?
Training, Validation, and Test Datasets
N-Fold Cross Validation
Early Stopping or When to Stop Training

Deep Learning – Initialization

What is Initialization?
Types of Simple Initializations
State-of-the-Art Method – (Xavier) Glorot Initialization

Deep Learning – Digging into Gradient Descent and Learning Rate Schedules

Stochastic Gradient Descent
Problems with Gradient Descent
Momentum
Learning Rate Schedules, or How to Choose the Optimal Learning Rate
Learning Rate Schedules Visualized
Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
Adam (Adaptive Moment Estimation)

Deep Learning – Preprocessing

Preprocessing Introduction
Types of Basic Preprocessing
Standardization
Preprocessing Categorical Data
Binary and One-Hot Encoding

Deep Learning – Classifying on the MNIST Dataset

MNIST: What is the MNIST Dataset?
MNIST: How to Tackle the MNIST
MNIST: Relevant Packages
MNIST: Model Outline
MNIST: Loss and Optimization Algorithm
Calculating the Accuracy of the Model
MNIST: Batching and Early Stopping
MNIST: Learning
MNIST: Results and Testing

Deep Learning – Business Case Example

Business Case: Getting acquainted with the dataset
Business Case: Outlining the Solution
The Importance of Working with a Balanced Dataset
Business Case: Preprocessing
Business Case: Preprocessing Exercise
Creating a Data Provider
Business Case: Model Outline
Business Case: Optimization
Business Case: Interpretation
Business Case: Testing the Model
Business Case: A Comment on the Homework
Business Case: Final Exercise

Deep Learning – Conclusion

Summary on What You’ve Learned
What’s Further out there in terms of Machine Learning
An overview of CNNs
DeepMind and Deep Learning
An Overview of RNNs
An Overview of non-NN Approaches
Download All Resources

Module 4 – Tableau / MS Power BI

Module 4 a) Tableau

Module 1 – Getting Started with Tableau

Overview Of Tableau
Tableau Architecture
Installation And Configuration Of Tableau 10

Module 2 – Connecting to The Data

Managing Metadata
Managing Extracts
Data Sources
Cross-Database Joins
Data Aggregation And Data Ports
Tableau Charts
Bar Charts and Stacked Bars Data Blending
Tree Maps and Scatter Plots
Individual Axes, Blended Axes, Dual Axes and Combinational Chart

Module 3 – Visual Analytics

Drill Down and Hierarchies
Sorting, Filtering and Grouping
Trend and Reference Lines
Forecasting and Clustering
Analysis with Cubes and MDX

Module 4 – Developing First Bar Chart

Connecting Tableau to Data File
Navigating Tableau
Calculated Fields
Adding Colors, Labels and Formatting

Module 5 – Time Series, Maps and Aggregation

Data Extracts and Time Series
Understanding Granularity, Aggregation and Level of Details
Default Location in Maps
Custom Geo Coding
Symbol Map and Filled Map

Module 6 – First Dashboard

Into Section
Joining Data In Tableau
Working With Maps and Hierarchies
Scatter Plot and Applying Filters in Different Sheets
Creating First Dashboard

Module 7 – Blending Data and Dual Axis Charts

Duplicate Values
Multiple Fields
Data Blending
Dual Axis Chart
Building Calculated Fields

Module 8 – Table Calculation and Storytelling

Downloading Dataset and Connection
Mapping
Building Table Calculation For Gender
Bins and Distributions for Age
Tree Map Chart
Advanced Dashboard
Storyline and Storytelling

Module 9 – Data Preparation

Data Format
Data Interpreter
Multiple Columns And Pivot
Metadata Grid
Advanced Data Preparation

Module 4 b) MS Power BI

A Quick Introduction

What is MSPBI and its scope
Learn the common work flow in MSPBI
Building blocks of MSPBI and its relations
Quick demo how to create a business dashboard in MSPBI
MSPBI components
Old vs. new technologies
Power BI Desktop/Service/Mobile

Getting Business Data

Get data in shape for use with MSPBI
Combining two or more data sets (source data) for reporting
Tackling messy data in MSPBI
Clean and transform data

Modelling in Power BI

How to connect many different data sources
Manage data source (database) relationships
Unique keys
Calculated columns and more
Custom calculations to evaluate time-based functions
Build calculated tables based on DAX formulas and expressions
Creating and viewing visuals easier with optimized models
Discover hierarchal drill-down tools for date fields

Data Visualization

Create and customize visualization in MSPBI and its power
Use combination charts
Create and format slicers with it
Map visualizations and its utilization
Use tables and matrixes
Long live bubbles
scatter charts in action
Advanced funnel and waterfall charts
Drive fast dashboard insights with gauges and numbers
Color your visualization world with colors
shapes and scales
Adding personal touch
logo etc. to reports and dashboards
Display and present your dashboard in a way you want with summarize data
Control how your report elements overlap with each other
Learn to drill into hierarchies
Manage how levels are shared (Z-order in reports)
How to use R visuals in MSPBI

Data Exploring and Sharing

Quick insights in Power BI Service
Create and configure a dashboard
Share dashboard with your organization
Display and edit visuals- tiles
full screen
Get more space on your dashboard
Install and configure a personal gateway
Excel and MSPBI
Import and excel table into Power BI
Import excel files with data models and power view sheets
Connect One Drive for business to MSPBI
Excel data in Power BI summary

DAX (Data Analysis Expression) Application

DAX and its basic building blocks
Create calculated columns (fields) and measures in MSPBI using DAX formulas
Breadth of functions available in DAX
User variables in DAX
Create expressions across multiple tables with relational functions;
Filter and evaluate tables using advanced table functions

Please find the Data Science Course details.

Duration of Module

Data Science Course Module	Data Science Course Duration
Course Module	Course Duration
R Programming –M1	35-45 (Hours)
Python Data Science M 2	50-55 ( Hours )
Tableau / Power BI –M4	25-30 (Hours)
R & Python	80-90 (Hours)
R + Python+Tableau	110-120 (Hours)
Ad. Data Science M 1-3	125-130 (Hours)
Note: GST Applicable on all Course Module

Batch Timings

MIS Analyst Schedule	MIS Analyst Timing
Monday to Friday	8:00 AM – 10:00 AM
Monday to Friday	10:00 AM – 12:00 PM
Monday to Friday	2:00 PM – 4:00 PM
Monday to Friday	4:00 PM – 6:00 PM
Saturday/Sunday	8:00 \| 12:00 \| 2:00 \| 4:00
Sunday	8:00 AM – 10:00 AM
Sunday	10:00 AM – 12:00 PM
Sunday	2:00 PM – 4:00 PM
Sunday	4:00 PM – 6:00 PM

Download Brochure

Adv Data Science

Python ML / Python Data Science

R Machine Learning

Data Analytics

Testimonials

Rahul Pal

From my point of view, I’ll strongly recommend SLA Consultants, the best Training Institute for Analyst courses. I am pursuing my Data Analyst Course from this institute under the guidance & knowledge of Data Analyst trainers, my two modules (advance Excel and VBA) of the course has been completed. I must say, they have good training faculty, as well as have the great infrastructure for learning and practicing.

Rahul Pal

Rohit Bhagat

I strongly recommend SLA, the best Training Institute for doing courses. I am doing my Data Analyst course in the Head Office, till now my two modules(Advanced Excel and VBA) have been completed. Its been good experience with a trainer, as they shared me a lot of knowledge in brief. At the institute, trainers and admin staff is very cooperative.

Rohit Bhagat

Deepak Sharma

I have completed Advanced Excel & Visual Basic Automation training course from SLA Consultants. I had amazing experience after completion of the training program and I would like to say thanks to the trainer, who elaborate solution for all the problems very quickly at the same time with very easy Technic to better understanding. Trainers are very kind and helpful. I would like to rank him 5 out of 5. I am so much obliged and feeling grateful, thanks a ton for the great support by trainers and thank you SLA Consultants.

Deepak Sharma

MIS & Data Analyst Executive

Amit Kumar

I hope, my review may help to those who are searching for an institution where they can enhance their skills and get a placement as well. I have joined SLA Consultants for complete MIS course, my trainer is one of the best teacher of my life. Who are searching for a institute joined it immediately without hassle & wasting your time. Trainer will teach you both theoretical as well as practical knowledge, also prepare you for interviews. The best thing about trainer is that they help you even after you completion of your course, share queries on e-mail or Whats-App your query and you will get your problem resolved. My over all experience was awesome.

Amit Kumar

MIS & Data Analyst Executive

Vinay Tiwari

My experience in SLA is quite well.I have taken classes for both modules Advance Excel and VBA Macros. Whenever I raised for any doubt and to be honest my all queries and doubt were sorted out patiently. I must say my trainer was quite supportive and patiently explained everything. Even though I have taken classes on weekend basis, but never feel like that the course is running or missing anywhere. And now I am too confident about Excel and VBA after completion of training under expert trainers. I will always recommend everyone to Join SLA Consultants if they are interested in MIS and RPA etc..

Vinay Tiwari

MIS & Data Analyst

Abhishek Bharti

I have great experience in SLA Consultants for Excel and VBA Macros with expert trainers.

Abhishek Bharti

MIS Executive

Hemant Harshwal

I have joined SLA Consultants for Advanced Excel and VBA. Have gone through no. of classes and got good knowledge about Excel and VBA. Our trainerwas very supportive and patiently explains every thing. He is very cooperative apart of training he also suggest regarding our career.

Hemant Harshwal

Data Analyst

Himanshu Nautiyal

I have done MIS & VBA course form SLA Consultants under the guidance of Anoop Sir, he has a very good knowledge & experience. He is a professional Trainer & his way of teaching is very good. It is great experience for me to grow with my skill & work efficiency. It is very good Platform who wants to build-up their career in MIS. Also thanks to Swati Mam for Co-ordinate with us and help me to find a good platform.

Himanshu Nautiyal

Fortune Marketing Pvt.Ltd.

Rajesh Pratap Singh

I’ve joined SLA Consultants for Advance excel and VBA It helps me to move my career. Especially Anoop Sir is very good teacher and his teaching style is awesome. One thing I want to tell you about new joiners who has no experience in Industry, they can build-up their career platform through SLA Consultants.

Rajesh Pratap Singh

Rahul Kumar

I have done MIS & VBA Training form SLA Consultants under the guidance of MR. ANOOP YADAV, he has a very good knowledge & experience in Corporate & MNC. He is professional Trainer & his way of teaching is as per the corporate requirement. I’d great experience for me to grow my skill & work efficiency. It is very good pelt from who wants to build-up their career in MIS.

Rahul Kumar

MIS Executive at ASIAN COLOUR COATED ISPAT LTD

Neeraj Gupta

SLA Consultants is too good & very helpful. If u want to achieve something in your life so please join today SLA Consultants. Especially I would like to thank Swati Mam, Anoop Sir.

Neeraj Gupta

MIS Executive at Shriram Automall India Ltd

Archit Khandelwal

Thanks to SLA consultants for giving me such type of opportunity, Great Institute with great teaching faculty. SLA is one of the class-apart institutions for those who learn something. I got a job in Fortune marketing Pvt Ltd as MIS Executive. SLA give me the confidence of ” I CAN “

Archit Khandelwal

MIS Executive at Fortune Marketing Pvt. Ltd

Anil Kumar

It was one of the best decisions to join SLA consultants for my course. The course that I pursued here was very well planned and executed. I’m highly satisfied with Anoop Sir. The lectures were easy to understand & industrial based & every doubt of mine was properly explained. I would recommend SLA consultants to all those who are willing to grow in their careers and do well.

Anil Kumar

MIS Coordinator at IQOR

Kumar Gautam

I joined MIS/VBA training at SLA. This type of practical oriented classroom training is very helpful & who’ll follow Anoop sir classes with this approach ensures that they will learn completely about subject in short period of time. “SLA Consultants” is the best for learning job-oriented classroom programs. Thanks to Anoop sir and Swati for the best placement support.

Kumar Gautam

MIS Coordinator at IQOR

Ummed Ali

SLA Consultants is better institute to learn practical training courses than any others. it provides the best training to their students. The teaching staff is very knowledgeable especially Anoop Sir for MIS and VBA courses. I learned a lot there and better career path.

Ummed Ali

MIS Executive at Evaxo pharma Pvt. Ltd

Bhavani Singh

The experience with SLA was phenomenal. It trained me to become industry ready and exposed me to various important work practices. The staff is extremely cordial and cooperative. The faculty members are the experienced and experts in the subjects taught by them. I thank SLA for the quality services and placement assistance provided by them.

Bhavani Singh

MIS Executive at G.D. FOODS MFG. (I) PVT LTD.

Anil Kumar Pal

Thanks to SLA consultants, my company values me a lot. I can churn out data from a huge database within a few minutes and keep the management happy. The ever cooperative trainers of SLA have made me capable of doing that and I will be grateful to them.

Anil Kumar Pal

MIS Executive at Recharge it Now

Priyanka Tiwari

I did not know what to do in life. Then I saw the ad of SLA Consultants. After getting knowledge of all their course offerings, I selected MIS and loved it from the start. Thank you for making my career and helping me get a good life SLA.

Priyanka Tiwari

MIS Executive at Concepts & Solutions

Santosh Kumar

When I joined, I didn’t have a clue about MIS Management and now my friends call me an expert of MIS. All thanks to SLA Consultants who filled me with great knowledge and software training without charging a lot of money.

Santosh Kumar

MIS Executive at Jai Shree Stationers Pvt. Ltd.

Kapil Negi

Data Management has always attracted me. SLA Consultants turned this hobby in a professional skill by teaching me all about MIS Management. I got a good job now and I am very happy with my life.

Kapil Negi

MIS Executive at Genpect

Aditya Pratap

I’ve completed the MIS training from SLA Consultants and now I believe that SLA provides the excellent & quality training. I got placed in a reputed company with complete placement support provided the SLA placement team.

Aditya Pratap

MIS Executive at CS Datamation Research Services Pvt Ltd

Avinash

The team of trainers working with SLA is amazing. The support staff is always there to help and Arbind Sir was a great guide. I will never forget my time at SLA and I thank them for the job they helped me get after I completed my course.

Avinash

MIS Executive at Serco Global Services

Tegbahadur Singh

My love for Data Management was turned into a career opportunity by SLA Consultants. They helped me learn all latest MIS software and made me a champion of Data Management. I have a great career path thanks to the SLA Team.

Tegbahadur Singh

MIS Executive at Renovision Automation

Sushil kumar yadav

Thanks to SLA consultants, my company values me a lot. I can churn out data from a huge database within a few minutes and keep the management happy. The ever cooperative trainers of SLA have made me capable of doing that and I will be grateful to them.

Sushil kumar yadav

MIS Executive at Bharti Infratel

Rajbir Sharma

SLA Consultants played the role of a friend, philosopher and guide in my career. They helped me learn complex software and complicated terminologies in such a manner that I now excel at MIS Management. I also love the job they have helped me get.

Rajbir Sharma

Sr. MIS Executive at Computer Junction Pvt. Ltd.

Pawan Gupta

I think SLA Consultants is the best place for skill development. They made me a professional from a fresher and I have seen them do the same for hundreds of students. I highly recommend them to very student or professional seeking career growth.

Pawan Gupta

MIS Executive - Rosmerta Technology Ltd.

Anand Kumar Jha

MIS Executive - Rosmerta Technology Ltd.

Nazia Praveen

From the first day I entered the office of SLA Consultants, they helped me. I completed the admission process swiftly and started learning MIS whenever it was convenient. Now I am working in a full time job that’s not hectic but enjoyable.

Nazia Praveen

MIS Executive - Globalite Retails Pvt Ltd

Hariom Agarwal

The staff, trainers, and management of SLA Consultants is great. These people really help freshers like me to get a good job. They have changed thousands of lives and I am happy to say that mine was also changed by them. So thank you.

Hariom Agarwal

MIS Executive - Globalite Retails Pvt Ltd

Indresh Kumar

I didn’t know anything about MIS management when I joined SLA Consultants. They helped me to learn the craft of managing, sorting and presenting data and now I am a valuable asset in my company. Thank You, SLA Consultants. I will always be grateful to you.

Indresh Kumar

MIS Executive - Recharge it Now

Md Sadik

SLA Consultants played the role of a friend, philosopher and guide in my career. They helped me learn complex software and complicated terminologies in such a manner that I now excel at MIS Management. I also love the job they have helped me get.

Md Sadik

MIS Executive at Evaxo Pharma Pvt. Ltd.