• Over 80 Percent of Companies Rely on Stale Data for Decision-Making
    Majority will go out of business in next 12 to 24 months.
  • 3 Steps to prevent Failure and Succeed
    • Extract & Audit Data
    • Data Cleansing
    • Accurate Data Reporting
  • Top 3% companies will survive and thrive and remaining will perish
  • Northstar Group can help you succeed with great reliability and user experience

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Not having proper data in production can result in the following pitfalls:

Inaccurate decision-making: If data is missing, inconsistent, or incorrect, it can lead to incorrect conclusions and poor decision-making.

  • Decreased efficiency: The lack of reliable data can make it difficult for organizations to streamline their processes and make the most of their resources.
  • Reputational damage: Inaccurate or incomplete data can damage an organization's reputation, especially if it leads to incorrect or embarrassing public statements or decisions.
  • Legal and regulatory issues: Organizations may be subject to penalties and fines if they are unable to comply with regulations and standards, such as those related to privacy and data protection.
  • Lost opportunities: If data is not properly managed, organizations may miss out on potential business opportunities, such as new markets, customer insights, and emerging trends.
  • Difficulty in evaluating performance: Poor data can make it difficult to assess the performance of individuals, departments, and the organization as a whole.
  • Reduced trust and collaboration: When data is unreliable, it can erode trust in the organization and reduce collaboration between departments and stakeholders.

Proper data management is critical for ensuring accurate and reliable data in production and avoiding these pitfalls.

NorthStar Services can help you setup an end to end Data Analytics Solution:

Data Analytic Services

In today’s digital world, data, analytics, play a key role in transforming native business operations, creating new business models and unleashing process improvements. NorthStar Group’s data, analytics I services, enable organizations to deliver value across the customers’ journey by empowering users with more agile and intuitive processes.

We leverage data using our data analytics services and solutions, which enables our clients to make informed decisions and offerings to achieve their business objectives. We build machine learning ML-powered models and frameworks and fine-tune decision-making models by aligning them with our clients business objectives.

Our Approach

Step 1 - Define your goals and objectives

What does your organization hope to achieve through an investment in data analytics? Answering this question will help you determine what type of data you need to collect and how you can use it to further your business goals.

Step 4 - Collect and analyze your data

This step will require you to establish processes for data collection, storage, and analysis. You may also need to develop custom algorithms or hire data analysts to help with this process. Self-service analytics tools can help speed up the process.

Step 2 - Determine who will be responsible for data analytics

Will you appoint a dedicated data analyst? Or will data analytics be a shared responsibility among different business teams? Assigning clear roles and responsibilities will help ensure that your data analytics strategy is executed effectively.

Step 5 - Communicate your results

This includes sharing your findings with decision-makers within your organization and developing plans for how to act on your insights. It's also important to document your process so that you can replicate your success in the future.

Step 3 - Choose the right tools and technologies

There are a wide variety of data analytics tools and technologies available, so it's important to choose the ones that best fit your needs. Consider what type of data you need to collect and analyze, then select tools that will make it easy to do so. You should also consider whether you need to invest in any new best in class software platforms to support your data analytics initiatives.

Final - Turn your strategy into a competitive advantage

A data analytics strategy is a vital part of any organization, but it can be difficult to know where to start. If you're looking for a platform to help make the process easier, it might be time to try NorthStar Group With its powerful search and AI capabilities, NorthStar Group can help you quickly create personalized, actionable insights from your data to make better decisions.

What we offer

Data Aggregation

After collecting data from disparate sources, our team of experts stitches the data together in a unified format to better analyze it. Our solutions are aimed at imparting consistency by employing pre-defined processes to collect and manage data across the data life cycle.

  • Collect structured and unstructured data from different primary and secondary sources.
  • Create a data master and provide data in a golden source that complies with regulatory requirements.
  • Input data into relevant databases to automate data ingestion, where possible.
Data Engineering

In this data-driven world, companies are compelled to harness the true potential of data to derive new insights and make effective decisions. This is where our data engineering services and solutions are instrumental in helping our clients make the best of unrealized opportunities.

  • We use product holding data, collection operation data, and collectors’ notes to build models, to enhance the predictive ability of the models.
  • We manage structured and unstructured data using natural language processing (NLP)-based algorithms.
  • We implement appropriate end-to-end data ingestion to data enrichment processes across domains using our data analytics & engineering solutions.
  • We provide automated data ingestion and integration solutions across multiple sources to build a data lake.
Business Intelligence and Reporting

Every modern-day business generates a gigantic volume of data. NorthStar Group with its expert data analytics services and solutions, helps these organizations to fuel their analytics projects and generate real-time reports, insights, and recommendations. NorthStar Group’s approach to business intelligence is driven by our client requirements.

  • Our solutions team interacts with each of our client’s stakeholders to understand the challenges, pain points, business goals, and the existing architecture.
  • We aim to provide scalable and cost-effective solutions for near-future growth
  • We aim to equip organizations with data, and, hence, we provide end-to-end services from engineering the data in the cloud to processing data, performing advanced analytics, creating deep learning models, and visualizing it for the organizations to recognize their trends and weaknesses and plan accordingly.

We are Northstar Inc also have training programs for working professionals who want to venture into Data Analytics field. Write to Amit@nstargroupinc.com to know more details and pricing information for the courses.

Topic 1 : Classical Machine Learning

(15 lectures. Total 25 taught hours)

  • 8 in-class hands-on tutorials.
  • 8 real life assignments.
  • Lecture 1 Introduction to machine learning (Theory) (90 Minutes).
  • Lecture 2 Introduction to machine learning (Theory) (90 Minutes).
  • Lecture 3 Multivariate Linear Regression (Theory) (90 Minutes).
  • Lecture 4 Logistic Regression (Theory).
  • Lecture 5 Logistic Regression (Theory).
  • Lecture 6 Decision Tree Models (Theory Sessions).
  • Lecture 7 Ensemble Methods in Decision Tree Models (Theory and Python Hands-on) (90 Minutes).
  • Lecture 8 K-Means Clustering Theory and Python Hands-on (90 Minutes).
  • Lecture 9 PCA Theory and Python Hands-on.
  • Lecture 10 Feature Engineering and Feature Selection (Theory Session) (90 Minutes).
  • Lecture 11 Model Evaluation (Theory Session) (90 Minutes).
  • Lecture 12 Support Vector Machines (Theory Session) (90 Minutes).
  • Lecture 13 An Introduction to Deep Learning (90 Minutes).

Topic 2 : Python for Data Science

(10 to 15 100% hands-on lectures. Total 15 to 20 taught hours)

  • 100% in-class hands-on tutorials.
  • 5 real life assignments.
  • Lecture 1 Python Fundamentals Crash-Course (2 hours).
  • Lecture 2 Python for Data Analysis NumPy (2 hours).
  • Lecture 3 Pandas (2 hours).
  • Lecture 4 Data Visualization with Matplotlib (2 hours).
  • Lecture 5 Data Visualization with Seaborn (2 hours).
  • Lecture 6 Pandas Built-in Data Visualization Functions (2 hrs).
  • Lecture 7 Major Project 3 to 5 hours.

Topic 3 : Neural Networks / Deep Learning using Tensorflow

(10 to 12 100% hands-on lectures. Total 10 to 12 taught hours)

  • in-class theory and hands-on tutorials
  • 4 real life assignments.
  • Lecture 1 An Introduction to Deep Learning.
  • Lecture 2 ANN Simple Artificial Neural Networks.
  • Lecture 3 CNN Convolution Neural Networks.
  • Lecture 4 RNN Recurrent Neural Networks .
  • Lecture 5 Neural Nets Advanced Coding.
  • Lecture 6 Deep Learning Advanced Topics.
  • Lecture 7 DL Hyperparameter Tuning.
  • Lecture 8 Transfer Learning (Deep Neural Networks).
  • Lecture 9 DL Major Project.

Topic 4 : Natural Language processing (NLP) with Bert and Transformers

(8 to 10 100% hands-on lectures. Total 10 to 12 taught hours)

  • in-class theory and hands-on tutorials
  • 5 real life assignments.
  • Lecture 1 Common NLP terminology.
  • Lecture 2 Components o NLP.
  • Lecture 3 Steps in NLP analysis.
  • Lecture 4 Lexical Analysis.
  • Lecture 5 Syntactic Analysis.
  • Lecture 6 Tokenization in NLP.
  • Lecture 7 NLP stop words.
  • Lecture 8 Bag of words.
  • Lecture 9 Stemming.
  • Lecture 10 Lemmatization.
  • Lecture 11 Part of speech tagging.
  • Lecture 12 Lexical & Syntactic analysis .
  • Lecture 13 Semantic Analysis – Step by Step.
  • Lecture 14 Homographs, homophones, and homonyms.
  • Lecture 15 Polysemy.
  • Lecture 16 Ontology.
  • Lecture 17 Word sense disambiguation.
  • Lecture 18 Chinking in NLP.
  • Lecture 19 Relation Detection in NLP.
  • Lecture 20 Term-document matrix (co-occurrence matrix).
  • Lecture 16 Co-occurrence matrix.
  • Lecture 21 Semantic Analysis (Python Hands On).
  • Lecture 22 Distributional Semantics and Word Embeddings.
  • Lecture 23 Latent Semantic Analysis.
  • Lecture 24 Topic modelling.
  • Lecture 25 Tf-idf Matrix.
  • Lecture 26 LSA with tf-idf (Hand-on).
  • Lecture 27 CBOW - Continuous Bag of Words Model.
  • Lecture 28 Skip-gram model.
  • Lecture 29 WORD2VEC.
  • Lecture 30 GloVe - Global Vectors for Word Representation.
  • Lecture 31 STEP 4 in NLP. Disclosure integration.
  • Lecture 32 STEP 5 in NLP. Pragmatic Analysis.
  • Lecture 33 Rule-bases vs. Statistical NLP.
  • Lecture 34 A complete NLP Python Hands On.
  • Lecture 35 BERY.
  • Lecture 36 SPACY.
  • Lecture 37 NLTK.
  • Lecture 38 Transformer Models in General.
  • Lecture 39 Advanced Concepta in NLP.
  • Lecture 40 Major Project.

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